Browsing by Author "Canizares, Claudio"

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An Energy Management System With Short-Term Fluctuation Reserves and Battery Degradation for Isolated Microgrids
(Institute of Electrical and Electronics Engineers (IEEE), 2021-08-10) Cordova, Samuel; Canizares, Claudio; Lorca, Alvaro; Olivares, Daniel E.
Due to the low-inertia and significant renewable generation variability in isolated microgrids, short time-scale fluctuations in the order of seconds can have a large impact on a microgrid’s frequency regulation performance. In this context, the present paper presents a mathematical model for an Energy Management System (EMS) that takes into account the operational impact of the short-term fluctuations stemming from renewable generation rapid changes, and the role that renewable curtailment and batteries, including their degradation, can play to counter-balance these variations. Computational experiments on the real Kasabonika Lake First Nation microgrid and CIGRE benchmark test system show the operational benefits of the proposed EMS, highlighting the need to properly model short-term fluctuations and battery degradation in EMS for isolated microgrids with significant renewable integration.
Behind-the-Meter Compressed Air Energy Storage Feasibility and Applications
(University of Waterloo, 2019-05-30) Anierobi, Chioma Christiana; Canizares, Claudio; Bhattacharya, Kankar
In many jurisdictions, commercial and industrial (C&I) customers are charged for their energy consumption as well as the power drawn from the grid at peak load hours. In Ontario, the demand-based charge component of the electricity cost has been skyrocketing, and this cost often accounts for a significant portion of the overall operating cost of large customers. The Ontario Government in 2010 launched the Industrial Conservation Initiative (ICI) program which requires large customers (Class A) to pay a Global Adjustment (GA) charge, based on their percentage contribution in load during the top five system peak load hours over a one-year base period. This offers enormous savings opportunity to many industrial customers by using strategies to reduce or offset their load during these system peak load hours. However, managing demand can be challenging when faced with production constraints in areas of high-energy sensitive production lines where short interruptions are not permitted. Energy Storage System (ESS) offers the customer the capability to carry out its usual operations while simultaneously saving on the electricity bill through demand reduction. ESS can provide electricity to the facility during system peak periods to reduce the power drawn from the grid, while during non-peak price periods, the ESS is recharged by harnessing the low-cost power. In this work, a detailed operations model of behind-the-meter Small Scale Compressed Air Energy Storage (SS-CAES) is developed for an industrial customer, with an existing well/cavern that can be re-purposed for air storage. The developed optimization model manages the operation of the CAES facility to minimize electricity costs, determining the storage energy output and the corresponding charging and discharging decisions of the SS-CAES system. Furthermore, a detailed economic analysis is carried out to examine financial viability of a practical behind-the-meter SS-CAES project. Some key parameters such as life cycle, CAES capacity and capital cost, and electricity price are considered for carrying out a sensitivity analysis, and the results suggest that SS-CAES is economically viable in the current Ontario rate structure. It is shown that the cost of an SS-CAES project and GA charges are the key determining factors for economic deployment of SS-CAES in Ontario.
A Decentralised Transactive Energy Market Considering Physical System Constraints
(University of Waterloo, 2022-08-29) Pankhurst, Colton; Bhattacharya, Kankar; Canizares, Claudio
Increasing levels of Distributed Energy Resources (DERs) are expected to play a key role in achieving global electricity decarbonisation goals, providing both a challenge and an opportunity for the electricity industry. Conventional approaches such as Net Energy Metering (NEM) have been questioned regarding their effectiveness in properly rewarding DERs, and larger efforts around the integration of DERs into wholesale markets do not address potential value streams at the distribution system level. Local energy markets leveraging direct Peer-to-Peer (P2P) trading have been proposed as a solution, which can increase prosumer participation in lower cost and more reliable supply of energy to consumers. Many approaches have been proposed to determine the optimal dispatch of distributed resources; however, a gap remains in the research to date on how to efficiently allow for prosumer decision autonomy while ensuring that the physical layer of the power system is considered. This thesis proposes a decentralised transactive solution that retains prosumer negotiation and decision autonomy, while using network operator and market determined prices to allocate limited system resources for a feasible, locally optimal system state. Peer-to-Utility (P2U) transactions are added to existing P2P energy frameworks to obtain transactive local peer decision criteria considering Peer-Centric (PC) and System-Centric (SC) objectives. Peers are able to interact with wholesale electricity market derived prices through P2U transactions, allowing for consideration of net export value in welfare maximising decisions. The proposed approach includes a split transaction fee pricing mechanism for virtual prosumer interactions that considers the networks characteristics such as topology and operational constraints to ensure consideration of the physical layer in peer decision making. In addition to pricing mechanisms for coupling the virtual and physical layers, a congestion clearing process is proposed, which coordinates with the decentralised transaction matching process and the Network Usage Charges (NUCs) to ensure efficient allocation of network capacity. Previously reported distribution networks are used to compare the transaction decisions, economic performance, and system performance of the proposed solution with existing approaches. The results demonstrate the effectiveness of the proposed method in ensuring system feasible, locally optimal transaction sets with prioritisation of local peers.
Dynamic Modelling and Performance Analysis of Energy Storage Systems for Frequency Regulation in Bulk Power Systems
(University of Waterloo, 2021-09-21) Guzman Encalada, Noela Sofia; Canizares, Claudio; Bhattacharya, Kankar
Renewable Energy Sources (RESs) provide a feasible alternative to supply electrical loads without the unfavorable environmental impacts of fossil fuels. However, despite the signiﬁcant environmental beneﬁts of RESs, several operational challenges associated with their high levels of penetration in power systems need to be addressed. Extensive research has shown that Energy Storage Systems (ESSs) facilitate increased penetration levels of RESs by providing ﬂexibility to the system, especially considering the technical maturity and decreasing cost of these technologies; hence, penetration of ESS, such as batteries and ﬂywheels is likely to grow signiﬁcantly in the coming years. Indeed, services that have been traditionally procured from synchronous generators such as Frequency Regulation (FR) are already being provided by ESSs. However, appropriate frequency control must be considered to take advantage of the fast response capability of ESS facilities, while coordinating their response with the bulk conventional generators currently used for FR. Some characteristics of the bulk power grids, regulation signals, and the State of Charge (SoC) management of the ESSs need be considered for the design of proper FR controls. In this thesis, a FR model is proposed of a large interconnected power system including ESSs such as Battery Energy Storage Systems (BESSs) and Flywheel Energy Storage Systems (FESSs), considering all relevant stages in the frequency control process. The model, which considers Communication Delays (CDs) in the transmission of signals in the FR control loop, is developed from the viewpoint of an Independent System Operator (ISO), using the Ontario Power System (OPS) as case study. To this eﬀect, empirically-based and generic SoC models for FESS and BESS considering the charging and discharging process characteristics are proposed. The system, ESSs, and SoC components are modelled in detail from a FR perspective and validated using real system and ESSs data, and a practical transient stability model of the North American Eastern Interconnection (NAEI) in Dynamic Security Assessment Tools (DSATools™) platform. The proposed model is validated with and considers all main stages of the FR control process, including CDs and the SoC management model of the ESS facilities, ensuring a realistic closed-loop response. Simulation studies show that the proposed model accurately represents the FR process of a large interconnected power system including ESSs, and can be used for accurate FR studies. The impact of CDs and SoC management of ESS facilities on the Area Control Error (ACE), and the computational eﬃciency of the proposed FR model are studied and discussed. A novel H2 ﬁlter design is proposed to optimally split the FR signal between conventional and fast regulating ESS assets, considering typical CDs. The design approach includes ﬁltering the FR signal by producing a slowly-varying component or Traditional Regulation Signal (RegA) to be provided to the slow regulating resources (i.e., Traditional Generators (TGs)), while the remaining fast component or Dynamic Regulation Signal (RegD) is provided to the fast response ESS facilities (FESS and BESS) to take advantage of their fast response characteristics. The design of the H2 ﬁlter is formulated as an optimal control design problem, and the proposed ﬁlter is integrated into the previously validated FR model with ESSs to form an Integrated Model, which includes a Proposed Set-Point (PSP) calculation and an anti-windup strategy. The PSP allows FR capacity from ESSs to be comparable to TGs FR capacity while keeping the system stable, which is not the case in the current FR process for the OPS. The proposed anti-windup strategy is added to avoid saturation when both TGs and ESSs reach their limits, or TGs reach their limits while the ESS facilities are not able to follow the PSP signals because of their SoC limits. Thus, the proposed ﬁlter sends RegA and RegD signals considering the SoC of fast response resources and capacity limits of ESSs and TGs, and depend on the conditions of the system, working in a coordinated manner. The FR performance with the H2 ﬁlter signals, RegA and RegD, is also compared with the existing FR process in the OPS, focusing on studying the impact of CDs and limited regulation capacity, and the eﬀect of the PSP calculation and anti-windup strategy. The results show that the H2 ﬁlter design and signal splitting strategy improves the FR process performance signiﬁcantly, in terms of reducing the ACE, and thus reduce the need for regulation capacity. Finally, a detailed methodology is developed to obtain Marginal Rate of Technical Substitution (MRTS) curves for the Independent Electricity System Operator (IESO). The IESO’s MRTS curves consider diﬀerent ESSs and discharging times (i.e., 15 min for FESS, and 15 min, 1 h, 2 h, and 4h for BESS), scenarios (i.e., peak hours, non-peak hours, morning ramp hours, and evening ramp hours), and seasons. The criteria agreed upon with the IESO for the generation of heat maps and MRTS is also presented. Furthermore, the procedure to select the representative typical days per season to be used in the generation of the MRTS curves is explained in detail, and an example of how to interpret one of the MRTS curves is explained. Heat maps and MRTS curves are proposed as analysis tools to allow ISOs to select the desired performance metric, and the combination of RegA and RegD resources that would allow to achieve it while still reducing the total regulation capacity. Although this methodology is applied to the IESO, it could be applied to other ISOs with appropriate modiﬁcations.
Electricity for All: Issues, Challenges, and Solutions for Energy-Disadvantaged Communities [Scanning the Issue]
(Institute of Electrical and Electronics Engineers (IEEE), 2019-09-05) Canizares, Claudio; Nathwani, Jatin; Kammen, Daniel
Millions of people have limited or no access to electrical energy. A diverse group of scholars and activists, from individual researchers to nongovernmental organizations (NGOs), civil society organizations, UN, and increasingly to engineering and financial private sector firms, have been working on initiatives to address this issue, which is considered by many a human rights problem. Energy access is not only a challenge for developing economies but is also equally important for service to remote areas such as the Arctic, islands, and communities distant from the grid. Several governments and private institutions, as well as nongovernment organizations around the world, are now funding projects and a variety of initiatives to help impoverished communities, especially across Africa and remote regions, develop clean and sustainable energy systems. The IEEE is actively involved in addressing energy access issues through several initiatives, such as the Smart Village program, focusing on “integrating sustainable electricity, education, and entrepreneurial solutions to empower off-grid communities” (http://ieee-smart-village.org/). In this special issue, we concentrate on highlighting the current state of knowledge associated with strategies for bringing clean, affordable, and sustainable electricity service reliably to energy-limited communities, based on local renewable energy (RE) sources and storage systems. Sociopolitical and economic forces as well as inclusionary and exclusionary culture or practices that often define the technical approaches and solutions offer opportunities for progress on this important problem.
Energy Management Systems for Multi-Microgrid Networks Under Uncertainties
(University of Waterloo, 2023-08-08) Ceja-Espinosa, Carlos; Canizares, Claudio; Pirnia, Mehrdad
Environmental concerns have motivated a gradual transformation of power systems in recent years, mainly focused on replacing fossil fuel-based energy sources with Renewable Energy Sources (RESs) such as solar and wind energy. However, due to their variable nature, the large-scale integration of RESs poses several technical challenges for the safe and efficient operation of evolving power systems. The adoption of microgrids (MGs) has increased as a viable option to effectively integrate RESs into existing grids and reduce the dependency on conventional, centralized power stations, as well as enhancing the electrical supply resiliency. Furthermore, MGs can provide sustainable energy to remote areas in which a connection to the main power grid is not possible. In this context, the Energy Management System (EMS) of the MG, which is responsible for determining its optimal operation, is an important part of MG control. However, the variability of electricity demand and RESs within an MG complicates the adequate dispatch of the MG resources to maintain supply-demand balance. Hence, uncertainties inherent to an MG must be taken into account, which is one of the main topics of this thesis. The coordinated operation of multiple MGs as a multi-microgrid (MMG) system has recently attracted attention due to the potential benefits that originate from a coordinated operation, as opposed to the individual and independent operation of each MG. The collective operation enables the possibility of power exchanges among MGs and the main grid, which can mitigate the unpredictability of RESs, as well as reduce the operational costs by taking advantage of the heterogeneity of load and generation profiles in each MG. Furthermore, differences in generation costs and grid buying/selling prices can incentivize power exchanges and ensure the maximum utilization of RESs. Therefore, it is important to design EMSs that adequately consider the collective operation of a set of MGs while taking uncertainties into account, which is the primary focus of this thesis. In the first part of this thesis, a centralized MMG EMS model is proposed, which is formulated as a cost minimization problem that considers the operation of all MGs and their interactions among each other and the main grid as a single system. The model includes detailed operational constraints of thermal generation units and Energy Storage Systems (ESSs), as well as power capacity limits at the Point of Common Coupling (PCC) of each MG. A decomposition procedure based on Lagrangian relaxation is then applied, with the goal of separating the complete problem into subproblems corresponding to each MG, which can be solved independently with minimal information exchange through a subgradient-based distributed optimization algorithm. Demand and solar irradiance data from a realistic Active Distribution Network (ADN) in São Paulo, Brazil, are then used to design a system to test and validate the proposed models. The simulation results show that the distributed algorithm converges to the optimal or a near-optimal solution of the centralized model, making the proposed approach a viable alternative for the implementation of a distributed MMG EMS. Furthermore, the advantages of an MMG system are demonstrated by showing that the operational costs of the system are significantly reduced when MGs are able to exchange power among each other and with the main grid, compared to their costs in individual operation. In the second part of this thesis, the proposed centralized MMG EMS model is reformulated using an Affine Arithmetic (AA) optimization framework to consider uncertainties associated with electricity demand and renewable generation. First, the uncertainties are characterized by their affine forms, which are then used to redefine the variables, objective function, and constraints of the original model in the AA domain. Then, the linearization procedure of the absolute values introduced by the AA operators is explained in detail. The proposed AA model is validated through comparisons with the deterministic and Monte Carlo Simulation (MCS) solutions. The test system used in the aforementioned MMG distributed dispatch approach is utilized to show that the AA model is robust under a range of possible realizations of the uncertain parameters, and can be solved with lower computational burden and in shorter execution times with respect to an MCS approach, while considering the same range of uncertainties, which is one the main advantages of the proposed AA model. Furthermore, it is demonstrated that the affine forms of the solution variables can be used to find a dispatch for different realizations of demand and renewable generation, with no need to repeatedly solve the optimization problem.
Frequency and Voltage Control of a Grid of Microgrids
(University of Waterloo, 2021-04-21) Alghamdi, Baheej; Canizares, Claudio
The rapid proliferation of Distributed Energy Resources (DERs) in recent years has resulted in significant technical challenges for power system operators and planners, mainly due to the particular characteristics of some of these systems that are interfaced with converters that alter the dynamic behavior of typically power systems. To accommodate the increasing penetration of DERs in power systems, microgrids have been formed to facilitate their integration. The operation of these microgrids could be further enhanced by interconnecting them to satisfy the overall system demand, and improve their stability if suitable control schemes are implemented. The control of microgrids has been extensively studied; however, coordinated operation, dynamics, and control of a grid that includes interconnected microgrids have not been sufficiently addressed in the literature, and thus this is the focus of this thesis. In the first stage of the thesis, a new microgrid interface based on Virtual Synchronous Generators (VSGs) is proposed to control the power exchange of interconnected ac and dc microgrids, and provide frequency support, voltage regulation, and virtual inertia for individual microgrids and the host grid as required, to improve both frequency and voltage dynamics for the overall system. Thus, a hierarchical distributed control technique is proposed, where the primary control of interfacing VSGs provides adaptive inertia for the ac systems, while a secondary distributed control of the system regulates the frequency and the voltages of the host grid and the interconnected microgrids, based on a consensus technique with limited information about the overall system. The proposed controller shares the total system load among the grid and microgrids, while minimizing the overall frequency and voltage deviations in all interconnected systems. The proposed interface and the controller are implemented, tested, and validated in detailed simulations for a grid-of-microgrids system. In the second stage of the thesis, an adaptive active power droop controller and voltage setpoint control in isolated microgrids for optimal frequency response and stability after disturbances is first proposed and presented, and then applied to the coordinated control of interconnected microgrids. The control scheme involves an optimal and model predictive control approach, which continuously adjusts the active power droop gains and the voltage setpoints of Distributed Energy Resources (DERs) to maintain the frequency of the system within acceptable limits, and enhance the primary frequency response of the system, while taking into account the active power sensitivity of the microgrid loads to the system's operating voltage. The proposed approach is also implemented, tested, validated, and compared via detailed simulations in a microgrid benchmark system and the developed grid-of-microgrids test system. The results demonstrate that the proposed VSG controlled interfaces limit severe frequency deviations during disturbances, and allow proper power sharing among the microgrids without causing significant power transients for the ac/dc systems, compared to existing techniques. Furthermore, the proposed secondary distributed and centralized frequency and voltage controllers maintain the power balance of the interconnected systems and regulate the microgrids' frequencies and dc voltages to nominal values, compared to conventional frequency controllers; however, the distributed control approach shows better overall frequency and dc-voltage dynamics and regulation than the centralized control approach.
Improved and Practical Energy Management Systems for Isolated Microgrids
(University of Waterloo, 2018-04-10) Solanki, Bharatkumar; Bhattacharya, Kankar; Canizares, Claudio
There are many remote communities around the world which do not have interconnection with the power grid because of technical and/or economic constraints, and thus have to manage their energy requirements independently, mainly from fossil-fuel-based and in some cases renewable-based generation, operating as isolated microgrids. The reliable and economic operation of a microgrid is handled by an Energy Management System (EMS), which includes scheduling and dispatching Distributed Energy Resources (DERs) such as Distributed Generators (DG), Energy Storage Systems (ESS), with controllable loads and demand response (DR), while maintaining appropriate reserve levels, and considering uncertainty in the forecast of renewables. Thus, this thesis focuses on developing comprehensive EMSs that consider Unit Commitment (UC), and Optimal Power Flow (OPF) constraints, smart load models for DR, and possible deviations in the forecast of renewable-based DGs. First, a mathematical model of smart loads in DR schemes is developed, based on a centralized and integrated UC and OPF EMS for isolated microgrids, to optimally dispatch generation and smart loads. These smart loads are modeled by a neural network (NN) load estimator as a function of the ambient temperature, time of day, Time of Use (TOU) price, and a peak demand constraint that the microgrid operator may set. A novel Microgrid EMS (MEMS) approach based on a Model Predictive Control (MPC) technique to manage forecast uncertainties is formulated; this tool yields optimal dispatch decisions of DGs, and ESS, and obtains optimal peak demand constraints for smart loads, considering power flow and UC constraints simultaneously. The impact of DR on the microgrid operation with the developed MEMS is studied using a CIGRE benchmark system that includes DERs and renewable-based generation, demonstrating its feasibility and advantages over existing EMS approaches, and showing the benefits of controllable loads in microgrids. In isolated microgrids, the network losses and voltage drops across feeders are relatively small. This feature is utilized through a novel linearization approach applied to the unbalanced power flow equations to propose practical EMSs. The proposed EMS models are Mixed Integer Quadratic Programming (MIQP) problems, requiring less computation time and thus suitable for online applications. The proposed practical EMS models are compared with a typical decoupled UC-OPF based EMS with and without consideration of system unbalancing. These EMS models, along with ``standard" EMS models, are tested and validated, using an MPC approach to account for forecast deviations, on the CIGRE medium voltage benchmark system and the real isolated microgrid of Kasabonika Lake First Nation (KLFN) in Northern Ontario, Canada. The presented results demonstrate the effectiveness, and practicability of the proposed models. In the third stage of the thesis, the impact of Electric Thermal Storage (ETS) systems on the operation of Northern Communities' microgrids is analyzed. A mathematical model of the ETS system is developed, in collaboration with a colleague from Karlsruhe Institute of Technology, and integrated into an EMS for isolated microgrids, in which the problem is divided into UC and OPF subproblems, to dispatch fossil-fuel-based generators, ESS, and ETS charging. To account for the deviations in the forecast of renewables and demand, an MPC technique is used. The proposed ETS-EMS framework is tested and studied on a modified CIGRE medium voltage benchmark system, which comprises various kinds of DERs, and on the real KLFN isolated microgrid system. It is shown that the ETS significantly reduces operating costs, and allows for better integration of intermittent wind and solar sources. Finally, equivalent CO2 emission models for fossil-fuel-based DG units are developed considering their individual emission characteristic and fuel consumption. These models are then integrated within a microgrid EMS model, together with constant energy, and demand shifting load models, to examine the possible impact of DR on the total system emissions and economics of a microgrid, using again an MPC approach to manage forecast uncertainties. The impact of including the developed emission models on the operation of an isolated microgrid, equivalent CO2 emissions, and costs are examined considering five different operating strategies. The proposed operating strategies are validated on a modified CIGRE medium voltage benchmark system, with the obtained results highlighting the effectiveness of the proposed EMS and also demonstrate the impact of DR on emissions and costs.
Modeling and Control of the Hybrid Power Flow Controller for Steady-state and Dynamic Studies and Applications
(University of Waterloo, 2017-12-19) Tamimi, Behnam; Canizares, Claudio
Flexible ac transmission system (FACTS) controllers offer new opportunities to better control power systems, and can address some of the critical challenges faced by the grid, especially in the context of smart grids. Furthermore, the smartening of distribution systems with distributed generation, storage devices, and intelligent loads have created challenges for the operation of distribution feeders, which can be addressed adequately with power-electronics based controllers and FACTS controllers for distribution systems. However, the capital intensive nature of these controllers is a major obstacle for a wide application of this technology in power systems. A cost effective FACTS controller has been introduced in the literature, which yields operating characteristics similar to those of the versatile Unified Power Flow Controller (UPFC). This device consists of converters as well as passive components, and is referred to as the Hybrid Power Flow Controller (HPFC). The study of the HPFC and its performance in electric power systems requires adequate and relevant models representing the device behavior according to the intended studies, such as steady state or time-domain dynamic analyses; moreover, the benefits of an HPFC are determined by its limits and operating constraints. Therefore, static and dynamic models of the HPFC for transmission and distribution system applications are proposed and studied in this work. First, steady-state models of the HPFC for power flow and optimal power flow (OPF) studies are proposed in this work, considering its multiple control modes and operating constraints. Thus, a strategy for control limit handling in power flow calculations is proposed, while considering a discrete passive shunt capacitor bank in the device. Moreover, an OPF model of the device is proposed and explained in detail, representing all the device control and physical limits as constraints in the mathematical formulation, so that the HPFC can be optimally dispatched as a part of the transmission system control assets; this model is used to determine the optimal ratings of the device based on a cost--benefit analysis. The proposed power flow and OPF models are tested and validated based on several loadability studies on a two-area benchmark test system. The HPFC power flow model is also tested and applied to a detailed model of Ontario grid and its neighboring networks with more than 6000 buses. The analyses demonstrate the application of the models for planning and operation studies, evaluating the performance and the effectiveness of the device based on realistic studies and scenarios. Second, the merits and the added value of the HPFC application to distribution systems are discussed here through detailed modeling and time-domain simulations, examining its impact on a distribution network under different conditions. Thus, a detailed dynamic representation of the HPFC is developed and implemented in PSCAD/EMTDC, describing and proposing control strategies to properly operate this controller in distribution system applications, such as an effective and simple procedure for starting-up the device. The developed model is used to demonstrate the effectiveness of the controller for solving problems in distribution systems, such as voltage sags associated with feeder faults and power flow fluctuations due to intermittent renewable generation using a benchmark network as an illustrative example.
Modeling and Operation of Ground Source Heat Pumps in Electricity Markets Considering Uncertainty
(University of Waterloo, 2022-11-17) Peralta Moarry, Dario; Bhattacharya, Kankar; Canizares, Claudio
Ground Source Heat Pump (GSHP) systems have grown in popularity and acceptance worldwide as an attractive option to replace conventional Heating Ventilation and Air Conditioning (HVAC) technologies due to their capacity to provide space heating and cooling in buildings and houses. Such GSHP systems may participate as a price-taker in electricity markets through a load aggregator to optimize their load demand, being able to provide grid services, such as load shifting. Therefore, aggregated GSHP systems have the potential, if properly designed, integrated, and applied, to yield energy and carbon savings in the energy market. However, the integration of such aggregated GSHP systems brings new challenges to operators, as it involves uncertainties on ambient temperature and electricity price forecasts, which can be highly volatile and thus impact the GSHP system operation and its participation in electricity markets. From a detailed literature review of GSHP applications for load management for residential users, it can be concluded that there are no works that discuss the operational performance of large-scale GSHP systems, modeled in detail, and their integration in electricity markets; additionally, none of the existing works have considered uncertainties in terms of ambient temperature and electricity price forecasts for the optimal operation of aggregated GSHP systems. After a comprehensive review of the relevant background related to GSHP systems, aggregator strategies in the electricity market, and optimization in the presence of uncertainties, in this thesis, a detailed mathematical model is presented of a GSHP with a vertical U-pipe Ground Heat eXchanger (GHX) configuration to provide residential space heating/cooling, integrating them into a load aggregator model. Based on this model, a two-stage operational strategy for the GSHP price-taker aggregator participating in Day-Ahead Market (DAM) and Real-Time Market (RTM) is proposed, to determine the optimal annual heating/cooling load dispatch to control the temperatures for a community of houses that minimizes the aggregator’s cost. Simulations are presented then of an aggregator’s optimal load dispatch with a conventional HVAC and the proposed GSHP alternative, considering comfort maximization vis-a-vis minimization of electricity costs, and showing the impact of each objective with respect to the dispatch of controllable loads, in-house temperature, and total procurement costs. Finally, a novel model based on Robust Optimization (RO) is proposed and developed, considering uncertainties in terms of the DAM and RTM electricity prices and hourly ambient temperature forecasts, which yields an optimum schedule that protects against the worst-case scenario for a given level of conservatism. The RO model is compared and validated in a realistic test system with respect to Model Predictive Control (MPC) and Monte Carlo Simulations (MCS) approaches that are traditionally used to manage uncertainty. It is shown that the proposed RO approach is computationally efficient compared to the MPC and MCS approaches, and properly accounts for the considered uncertainties, demonstrating the advantage of the presented RO technique for GSHP dispatch by aggregators.
Modeling of Compressed Air Energy Storage for Power System Performance Studies
(University of Waterloo, 2020-04-16) Calero, Ivan; Canizares, Claudio; Bhattacharya, Kankar
In the effective integration of large renewable generation for grid scale applications, pumped-storage hydro and Compressed Air Energy Storage (CAES) are currently economically and technically feasible alternatives to properly manage the intrinsic intermittency of energy sources such as wind or solar, with CAES being less restrictive in terms of its location. Furthermore, the relative fast response, and the possibility of physically decoupling the charging and discharging drive trains interfacing the grid through synchronous machines make CAES a suitable asset to provide ancillary services in addition to arbitrate, such as black start, spinning reserve, frequency regulation, and/or voltage regulation. Nevertheless, although the economic value of CAES having multiple stream revenues has been studied in the context of planning and operation of power systems, the actual impact of CAES facilities on the electrical grids have not been properly addressed in the literature, in part due to the lack of suitable models. The existing CAES models proposed for power system studies fail to represent the dynamics, nonlinear relations, and physical restrictions of the main mechanical subsystems, by proposing simplifications that result in unrealistic dynamic responses and operating points when considering the entire CAES operating range, as is required in most ancillary services or during grid disturbances. Furthermore, the detail of these models and the controls used are inconsistent with the state-of-the-art modeling of other storage technologies such as batteries and flywheels. Hence, in order to bridge the gap in CAES modeling and control, this thesis propose a comprehensive physically-based dynamic mathematical model of a diabatic CAES system, considering two independent synchronous machines as interface with the grid, which allows simultaneous charging and discharging of the cavern, such as the recently inaugurated 1.75 MW CAES plant in Goderich, Ontario. Detailed and simplified models are proposed based on the configuration of the Huntorf plant, in Germany, which is one of the only two existing large CAES facilities currently operating in the world. The system modeled comprises a multi-stage compressor with intercoolers and aftercooler, driven by a synchronous motor in the charging stage, an underground cavern as storage reservoir, a multi-stage expander with a recuperator and reheater between stages, and a synchronous generator in discharging mode, such as the aforementioned small CAES Ontario plant. The proposed thermodynamic-based dynamic models of the compressors and expanders allow calculating internal system variables, such as pressures, temperatures and power, some of which are used as controllable variables. Furthermore, different approximations to model the nonlinear relations between mass flow rate, pressure ratio, and rotor speed in the CAES compressors and expanders, determined by so called “maps”, are proposed based on Neural Networks and physically-based nonlinear functions; these constrain the operation of the turbomachinery, but are usually ignored in existing models. A control strategy for active and reactive power of the CAES system is also proposed. The active power controller allows primary and secondary frequency regulation provision by the turbine and compressor. Special controllers are proposed to restrict the charging and discharging power of the turbine and compressor, to avoid pressure ratios that violate the restriction imposed by the cavern pressure. A surge detection controller for the compressor, and a controller that regulates the inlet temperature at each expansion stage are also presented, and these controls are complemented by a state of charge logic controller that shuts down the compressor or turbine when the cavern is fully charged or runs out of air, respectively. A coordinated droop-based reactive power control is also proposed for the parallel operation of the two synchronous machines, which is used to provide voltage regulation assuming both machines operate synchronized with grid. Finally, the implementation of a block-diagram based CAES model for transient stability studies in the DSATool’s TSAT® software is presented, based on a generic model architecture of the different CAES system's components and their interrelations. The performance of the proposed models, with different levels of detail, are examined in various electrical system studies. First, the potential of a CAES system to provide primary and secondary frequency regulation in a test power system with high penetration of wind generation is evaluated in Simulink®, where the proposed CAES models are also compared with existing models. The voltage regulation, oscillation damping capability, and frequency and transient stability impact of CAES are also studied in a modified WSCC 9-bus test system using TSAT®. It is demonstrated that CAES is more effective than equivalent gas turbines to regulate frequency and voltage and damp low frequency oscillations, with the simultaneous charging and discharging operation significantly reducing the frequency deviation of the system in the case of large power variations in a wind farm. Furthermore, the effects on the overall frequency regulation performance of incorporating detailed models for some of the CAES components, such as expansion air valve, compressor and turbine maps and associated controls is also assessed, demonstrating how modeling these sub systems restricts the CAES response, especially in charging mode. Finally, the effect of the stage of charge control on the frequency stability of the system for different cavern sizes is investigated, concluding that if the power rating of the CAES system is large enough, small cavern sizes may not allow proper provision of frequency regulation.
Optimal Operation of Power Distribution Feeders with Smart Loads
(University of Waterloo, 2016-12-21) Mosaddegh, Abolfazl; Canizares, Claudio; Bhattacharya, Kankar
Distribution systems have been going through significant changes in recent years, moving away from traditional systems with low-level control toward smart grids with high-level control, with improved technologies in communications, monitoring, computation, and real-time control. In the context of smart grids, Demand Response (DR) programs have been introduced so that customers are able to control and alter their energy consumption in consideration with distribution system operators, with benefits accruing to both customers and Local Distribution Companies (LDCs). This thesis focuses on the integration of DR with the intelligent operation of distribution system feeders. Thus, it proposes a mathematical model of an unbalanced three-phase distribution system power flow, including different kinds of loads and other components of distribution systems. In this context, an unbalanced three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which includes the models of lines, transformers, voltage-based loads, smart loads, Load Tap Changers (LTCs), and Switched Capacitors (SCs), together with their respective operating limits, to determine the optimal switching decisions for LTCs, SCs, and control signals for smart loads, in particular, Energy Hub Management System loads and Peaksaver PLUS loads. Hence, Neural-Network-based models of controllable smart loads, which are integrated into the DOPF model are proposed, developed, and tested. Since the DOPF model has different discrete variables such as LTCs and SCs, the model is a Mixed-Integer Non-Linear Programming (MINLP) problem, which presents a considerable computational challenge. In order to solve this MINLP problem without approximations and ad-hoc heuristics, a Genetic Algorithm (GA) is used to determine the optimal control decisions of controllable feeder elements and loads. Since the number of control variables in a realistic distribution system is large, solving the DOPF for real-time applications using GA is computationally expensive. Hence, a decentralized system with parallel computing nodes based on a Smart Grid Communication Middleware (SGCM) system is proposed. Using a "MapReduce" model, the SGCM system executes the DOPF model, communicates between the master and the worker computing nodes, and sends/receives data amongst different parts of the parallel computing system. When large number of nodes are involved, the SGCM system has a fast performance, is reliable, and is able to handle different fault tolerance levels with the available computing resources. The proposed approaches are tested and validated on a practical feeder with the objective of minimizing energy losses and/or energy drawn from the substation. The results demonstrate the feasibility of the developed techniques for real-time distribution feeder control, highlighting the advantages of integration of smart loads in the operation of distribution systems by LDCs.
Planning of Multi-Microgrids Considering Uncertainties and Spatial Characteristics
(University of Waterloo, 2023-03-13) Vera, Enrique; Canizares, Claudio; Pirnia, Mehrdad
Global warming is a serious issue that is being tackled from various fronts, one of them is the decarbonization of electrical energy systems, which may be addressed by introducing clean Distributed Energy Sources (DERs) such as Renewable Energy Resources (RESs) and Energy Storage Systems (ESSs). These types of technologies can be clustered to form Microgrids (MGs), which have proven to be technically and financially feasible solutions to supply electricity demand while reducing emissions and increasing resiliency. MGs can operate isolated or connected to the grid, both in rural and urban settings, which allows them to interact with the existing electricity grid to enhance its capabilities and functionalities, while improving power quality, reducing network congestion, increasing efficiency, reliability, and flexibility, and delaying investments in transmission and distribution systems. Hence, this thesis focuses on various relevant and timely aspects of MG planning, in particular for isolated Remote Communities (RCs) and for the interconnection of MGs and their integration with Active Distribution Networks (ADNs) to form Multi-Microgrid (MMG) systems. The deployment of clean MGs to satisfy RC electricity needs, considering their inherent geographic characteristics, imposes a series of challenges that must be taken into account when planning them. Thus, delivering electricity to RCs is economically and environmentally expensive, as the main source of electricity is diesel generators, which present significant Greenhouse Gas (GHG) emissions, and Operations and Maintenance (O\&M), transportation, and fuel costs. Therefore, an optimization model for the long-term planning of RC MGs to introduce RESs and ESSs is proposed in this thesis, with the objective of reducing costs and emissions. The presented model considers lithium-ion batteries and hydrogen systems as part of ESSs technologies. The model is used to investigate the feasibility of integrating these DERs in an MG in Sanikiluaq, an RC in the Nunavut territory in Northern Canada, where several planning scenarios with various combinations of resources are considered in order to assess the impact of different technologies. The results show that wind resources along with solar and storage technologies can play a key role in satisfying RC electricity demand, while significantly reducing costs and GHG emissions. Independent MGs can be interconnected to form MMG systems in the context of ADNs, bringing valuable benefits such as energy use, power quality and stability improvements, as well as flexibility and thus economic enhancements for both costumers and utilities. Therefore, a Two Stage Stochastic Programming (TSSP) model is proposed for the planning of MMGs within ADNs at Medium Voltage (MV) levels to minimize the total costs, while benefiting from interconnections of MGs and considering uncertainties associated with electricity demand and RESs. Furthermore, the model includes long-term purchase decisions and short-term operational constraints, using Geographical Information Systems (GIS) to realistically estimate rooftop solar limits. The planning model is used to study the feasibility of implementing an MMG system consisting of 4 individual MGs at an ADN in a municipality in the state of São Paulo, Brazil. The results show that the TSSP model tends to be less conservative than the deterministic model, which is based on simple and pessimistic reserve constraints, while being computationally more efficient than the usual, Stochastic Linear Programming (SLP) and Monte Carlo Simulations (MCS) approaches, with adequate accuracy. Finally, the MMG planning model at MV is further extended to include the Low Voltage (LV) grid. Thus, a model is proposed for the realistic planning of MMGs in the context of ADNs, with the assistance of GIS. The model considers the distribution system grid with an adequate level of detail for multi-year planning as well as the geographic features of the studied region. Similar to the MV model, it also includes long-term purchase decisions and short-term operational constraints, and considers uncertainties associated with electricity demand and RESs using a TSSP approach. GIS along with Deep Learning (DL) are used to more accurately estimate the rooftop areas within the studied region for solar PV deployment, as well as for modelling the LV grid. The planning model is then used to study in more detail the feasibility of implementing the MMG system previously considered in São Paulo, Brazil. The results of the extended TSSP LV grid model are compared with the results obtained using MCS and the less detailed TSSP MV grid model, demonstrating that both TSSP solutions are close to those obtained with MCS at a lower computational cost, while providing accurate and practical planning results.
Predictive Maintenance of Wind Generators based on AI Techniques
(University of Waterloo, 2019-12-17) Mammadov, Emin Elmar oglu; Canizares, Claudio
As global warming is slowly becoming a dangerous reality, governments and private institutions are introducing policies to minimize it. Those policies have led to the development and deployment of Renewable Energy Sources (RESs), which introduces new challenges, among which the minimization of downtime and Levelised Cost of Energy (LCOE) by optimizing maintenance strategy where early detection of incipient faults is of significant intent. Hence, this is the focus of this thesis. While there are several maintenance approaches, predictive maintenance can utilize SCADA readings from large scale power plants to detect early signs of failures, which can be characterized by abnormal patterns in the measurements. There exists several approaches to detect these patterns such as model-based or hybrid techniques, but these require the detailed knowledge of the analyzed system. As SCADA system collects large amounts of data, machine learning techniques can be used to detect the underlying failure patterns and notify customers of the abnormal behaviour. In this work, a novel framework based on machine learning techniques for fault prediction of wind farm generators is developed for an actual customer. The proposed fault prognosis methodology addresses data limitation such as class imbalance and missing data, performs statistical tests on time series to test for its stationarity, selects the features with the most predictive power, and applies machine learning models to predict a fault with 1 hour horizon. The proposed techniques are tested and validated using historical data for a wind farm in Summerside, Prince Edward Island (PEI), Canada, and models are evaluated based on appropriate evaluation metrics. The results demonstrate the ability of the proposed methodology to predict wind generator failures, and the viability of the proposed methodology for optimizing preventive maintenance strategies.
Primary and Secondary Frequency Control Techniques for Isolated Microgrids
(University of Waterloo, 2017-01-18) Farrokhabadi, Mostafa; Canizares, Claudio; Bhattacharya, Kankar
Isolated microgrids have been shown to be a reliable and efficient solution to provide energy to remote communities. From the primary control perspective, due to the low system inertia and fast changes in the output power of wind and solar power sources, isolated microgrids' frequency can experience large excursions and thus easily deviate from nominal operating conditions, even when there is sufficient frequency control reserves; hence, it is challenging to maintain frequency around its nominal value. From the secondary control perspective, the generation scheduling of dispatchable units obtained from a conventional Unit Commitment (UC) are considered fixed between two dispatch time intervals, yielding a staircase generation pro file over the UC time horizon; given the high variability of renewable generation output power, committed units participating in frequency regulation would not remain fixed between two time intervals. The present work proposes techniques to address these issues in primary and secondary frequency control in isolated microgrids with high penetration of renewable generation. In this thesis, first, a new frequency control mechanism is developed which makes use of the load sensitivity to operating voltage and can be easily adopted for various types of isolated microgrids. The proposed controller offers various advantages, such as allowing the integration of significant levels of intermittent renewable resources in isolated/islanded microgrids without the need for large energy storage systems, providing fast and smooth frequency regulation with no steady-state error, regardless of the generator control mechanism. The controller requires no extra communication infrastructure and only local voltage and frequency is used as feedback. The performance of the controller is evaluated and validated using PSCAD/EMTDC on a modified version of the CIGRE benchmark; also, small-perturbation stability analysis is carried out to demonstrate the contribution of the proposed controller to system damping. In the second stage of the thesis, a mathematical model of frequency control in isolated microgrids is proposed and integrated into the UC problem. The proposed formulation considers the impact of the frequency control mechanism on the changes in the generation output using a linear model. Based on this model, a novel UC model is developed which yields a more cost e efficient solution for isolated microgrids. The proposed UC is formulated based on a day-ahead scheduling horizon with Model Predictive Control (MPC) approach. To test and validate the proposed UC, the realistic test system used in the first part of the thesis is utilized. The results demonstrate that the proposed UC would reduce the operational costs of isolated microgrids compared to conventional UC methods, at similar complexity levels and computational costs.
Primary Frequency Control with Flywheel Energy Storage Technologies
(University of Waterloo, 2017-08-25) Peralta Moarry, Dario; Canizares, Claudio; Bhattacharya, Kankar
Over the last decade, concerns about greenhouse gas emissions have increased. Different strategies have been developed to minimize those effects, leading to the development of renewable energy sources worldwide. In recent years, the deployment of solar photovoltaic and wind energy-based renewable generation technologies have been growing at a fast pace. The penetration of these technologies into the power system network introduces new challenges for frequency and voltage stability because of the intermittency of these energy sources, and the increasing risk of significant voltage/frequency variations. The significant penetration of renewable sources requires fast regulation of the frequency deviations; hence, the implementation of primary frequency controls is necessary. There exists different techniques and strategies for primary frequency control, where governor regulation and under frequency load shedding are two of the best known, but these have several limitations regarding fast response. Thus, new control techniques based on energy storage systems, which are able to provide fast frequency control, are being studied. In this context, a flywheel energy storage (FES) system is studied and modeled in this thesis for frequency control in power systems, using the well-known software Dynamic Simulation Assessment Tool (DSATools)®, to allow researchers and practitioners to readily model FES in power system studies, particularly the Independent Electric System Operator (IESO) of Ontario. The proposed FES DSATools® model is tested and compared using a previously proposed test system with a large wind energy system (WES), which creates significant frequency and voltage fluctuations due to its characteristics. The FES stores and delivers energy to the power system, as required by the network, through a back-to-back power electronic converter system. A frequency/speed limiter controller is used, considering the network frequency deviation and the FES rotational speed in the active control of the flywheel-side converter for active power control of the flywheel. A static var compensator (SVC) for voltage control is also studied. The presented studies consider disturbances from sudden changes in the wind speed, which affect the WES output active power, creating considerable problems for the test system's stability. The simulation results suggest that the proposed FES model implemented on the system studied, provides effective primary frequency control, and it also improves the network voltage. Thus, the FES is shown to maintain system stability, increasing the operational efficiency of conventional and renewable generators.
Renewable Energy Integration in Canadian Remote Community Microgrids: The Feasibility of Hydrogen and Gas Generation
(Institute of Electrical and Electronics Engineers (IEEE), 2020-12-02) Vera, Enrique Gabriel; Canizares, Claudio; Pirnia, Mehrdad
Approximately 1.1 Billion, or 14%, of the global population do not have access to electricity due to the challenges associated with energy supply. Around 84% of those without electricity access reside in rural areas, with more than 95% being in sub-Saharan Africa and the developing parts of Asia. In Canada, about 72% of off-grid aboriginal and nonaboriginal communities use fossil fuel (oil: 71%, natural gas: 0.8%) as their main source of electricity generation, and only 4.7% of these communities rely on renewable energy sources (RES). In addition, 17.9% fulfill their energy demand through interconnections with other communities as they don?t have enough resources to support their own needs. The remaining 5.6% are reported to rely on unknown sources of electricity (see Arriaga et.al. 2014).
Self-Scheduling Operations of a Compressed Air Energy Storage Facility Under Uncertainties
(University of Waterloo, 2022-01-25) Zambroni, Matheus; Canizares, Claudio; Bhattacharya, Kankar
High penetration of Renewable Energy Sources (RES) such as solar and wind in power systems reduce carbon emissions and decentralize the energy generation. However, the intermittency of these sources introduces new challenges, since solar power is only available during sunlight hours and wind power is difficult to forecast and may present a high variability. Thus, since RES generation is not dispatchable, a power system with large RES penetration may not meet the demand at peak hours and experience voltage flickers and frequency fluctuations. To tackle these challenges, various Energy Storage Systems (ESS) technologies have been developed and deployed at different scales throughout the grid, providing either energy arbitrage or frequency regulation services to the power system. For the former, there are two mature large-scale ESS: pumped hydro storage and Compressed Air Energy Storage (CAES), with the latter being less restrictive in terms of location. Despite being a mature technology, there are only two large-scale CAES facilities worldwide. However, with the challenges modern power systems face nowadays, the bulk capacity, fast response, and efficiency of CAES facilities makes them an attractive ESS alternative. Electricity prices vary throughout the day depending on the system demand. At low demand, low-cost generators operate, resulting in cheaper electricity; at peak demand, more expensive units operate, hence increasing electricity prices. These electricity price variations opens opportunities for pro table businesses. In this context, an ESS facility owned by a private investor, depending on its capacity compared to the overall system, may participate as a price-taker or as a price-maker in electricity markets. Due to its bulk capacity, CAES can provide energy arbitrage to the grid and participate in the energy and reserve markets. Also, CAES newer designs decouple the charging and discharging processes using two synchronous machines, providing enhanced frequency regulation services. Since a CAES facility presents physical limitations, its optimum daily schedule must be determined a priori. Given the day-ahead electricity price forecast, an optimum schedule can be determined through self-scheduling models, where the daily pro t of the facility is maximized, which requires that the facility be properly modeled. Furthermore, with the high penetration of RES new and large sources of uncertainty have been introduced, particularly in generation and real-time market prices. Therefore, these uncertainties must be properly considered in CAES modeling and operation. In this thesis self-scheduling models for a price-taker CAES facility, that partakes in energy and reserve markets under electricity price uncertainties, are proposed. Using an existing non-linear model for a CAES facility, Robust Optimization (RO) is employed to represent price uncertainties, yielding an optimum schedule that protects against the worst-case scenario for a given level of conservatism. The model is benchmarked with Monte Carlo Simulations (MCS), presenting a lower computational burden while computing scenarios that the MCS fails to obtain. Thereafter, a novel linear thermodynamic model for the CAES is proposed, using mathematical tools for linearization such as McCormick Envelopes and linear-piecewise approximation, which compared with an existing non-linear model, it yields similar results at significantly lower computational costs. The novel model is further expanded considering uncertainties in electricity prices using RO and Affine Arithmetic (AA) approaches. The AA method keeps track of correlated uncertainties, yielding an optimum range of schedule with adjustable power dispatch for given real-time mismatches in price forecasts. Both methods are compared and benchmarked with the MCS approach, presenting significantly lower computational costs, with pro t intervals obtained from AA being more conservative than MCS and RO, i.e., the former method envelops the intervals obtained from the latter techniques. The CAES pro t and schedules for different levels of initial and fi nal State of Charge (SOC) of the facility are then assessed in order to estimate an ideal SOC level where the facility may maximize its participation and pro fit. Finally, a Principal Components Analysis (PCA)-Affine Policy (AP)-based self scheduling model for the CAES facility is proposed. PCA is a knowledge extraction based mathematical tool to reduce the dimension of a mathematical model by removing the less relevant variables, which may decrease the accuracy of the model. The method of AP, similar to AA, keeps track of correlated uncertainties and provides an optimum range of schedule with adjustable power dispatch for real-time mismatches in price forecast. The PCA-AP model is compared with AA and MCS, which is computational more expensive compared with AA, provides a tighter interval of pro t, hence ensuring a safer margin of operation in pessimistic scenarios. Compared with the MCS, similar results were obtained at a lower computational cost. The operation of a CAES facility charging and discharging concurrently is then examined, which offers the facility with a larger set of combinations for its operational states, and hence greater pro t, but at increased computational costs.
Smart Charging of Plug-in Electric Vehicles in Distribution Systems Considering Uncertainties
(University of Waterloo, 2016-05-05) Mehboob, Nafeesa; Canizares, Claudio; Rosenberg, Catherine
Distribution feeders and equipment are designed to serve peak loads, and in the absence of Plug-in Electric Vehicle (PEV) loads, day-ahead dispatch of feeders is typically performed by optimizing feeder controls for forecasted load profiles. However, due to climate change concerns, the market share of PEVs is expected to increase, and consequently, utilities expect an increase in demand due to these loads charging from the grid. Uncontrolled charging of PEVs may lead to new peaks in distribution feeders, which would require expensive infrastructure and equipment upgrades. Furthermore, PEV loads will represent new sources of uncertainty, temporal and spatial, which will pose a challenge for the centralized control and optimal operation of the grid. In practice, these uncertainties arise as a result of variability in factors such as the number of PEVs connected to the grid for charging, the arrival and departure times of PEVs, and the initial battery State-of-Charge (SoC). Hence, the integration of PEVs into the existing distribution system, without significant infrastructure upgrades, will be possible only through smart charging of these loads, while properly accounting for these uncertainties. The elasticity of PEVs provides a level of flexibility that can be used by utilities or Local Distribution Companies (LDC) to ensure efficient feeder operation, while providing fair and efficient charging to PEV customers. This thesis presents a novel two-step approach for the fair charging of PEVs in a primary distribution feeder, accounting for the uncertainty associated with PEVs, considering the perspectives of both the LDC and the PEV customer. In the first step of the proposed approach, the mean daily feeder peak demand and corresponding hourly feeder control schedules, such as taps and switched capacitor setpoints, are determined hourly, while minimizing the daily peak demand, taking the existence of PEVs into account. As an alternative to the conventional Monte Carlo Simulations (MCS), a nonparametric Bootstrap technique is used in conjunction with a Genetic Algorithm (GA)-based optimization model, to account for variations in the arrival and departure times, and the initial battery SoC of PEVs, at each node. In the second step, the maximum possible power that can be given to the charging PEVs at each node, while maintaining the peak demand value and corresponding feeder dispatch schedules defined in the first step, is computed every few minutes and shared fairly among the PEVs. The proposed technique is validated using the IEEE 13-bus test feeder as well as the distribution feeder model of an actual primary feeder in Ontario, considering significant PEV penetration levels. The potential gain in PEV charging efficiency is quantified for the proposed Bootstrap feeder control schedule with respect to the base schedule (without PEVs). The presented optimization approach is also compared with the current industry practice in Ontario, and a sensitivity-based heuristic technique, demonstrating the advantages and feasibility of the presented technique. The results show that the proposed approach could be implemented in practice due to its reasonable computational burden, and its ability to charge PEV loads better than the current industry practice, or a popular heuristic method, while satisfying feeder and peak demand constraints.
Smart Operation of Four-Quadrant Electric Vehicle Chargers in Distribution Grids
(University of Waterloo, 2017-05-10) Restrepo Restrepo, Mauricio; Canizares, Claudio; Kazerani, Mehrdad
Many policies and programs adopted in the context of climate change mitigation and substitution of fossil fuels are contributing to the continuous development and growth of Electric Vehicles (EVs) in urban mobility systems, reaching 1.26 million units on the roads through the end of 2015. Even though the increasing number of EVs will create problems in distribution systems, which can be mitigated using smart charging strategies, there will also be economic opportunities for EV owners to provide services to the grid while their vehicle are parked and plugged in, a concept known as Vehicle-to-Grid (V2G). Most of the studies on V2G have concentrated on the provision of services such as frequency regulation or spinning reserves, which may reduce the battery life because of the required extra charging/discharging cycles, and little attention has been paid to the possibility of providing reactive power control services to the grid by using the ac/dc converter and the dc link capacitor available in most advanced chargers, a practice that does not compromise the vehicle battery life. These kinds of chargers, which are known as four-quadrant EV chargers due to the capability of being operated in all quadrants of the P-Q plane, can be used in distribution networks to improve the power factor and help regulate voltage, thus facilitating larger EV penetrations, as discussed in this thesis. In the first part of this thesis, a new average model of a single-phase, four-quadrant EV charger is developed. The steady-state and step responses of the proposed model for different P-Q requests, corresponding to the operation in the four quadrants of the P-Q plane, are used to validate its performance against a four-quadrant EV charger prototype. The model is shown to be useful for efficient time-domain simulations and studies that include a number of EV chargers, such as EV integration studies in Low-Voltage (LV) distribution networks. A practical case study is presented to demonstrate and test the performances of the four-quadrant charger and its model, investigating the voltage interactions of several chargers in an LV residential network during the provision of three vehicle-to-grid (V2G) strategies for active and reactive power. In the second part, a novel three-stage algorithm to coordinate the operation of four-quadrant EV chargers with other volt/var control devices in Medium-Voltage (MV) and LV distribution feeders is proposed. The first stage of the algorithm is operated on a day-ahead basis and defines the Load Tap Changer (LTC) and capacitor schedules while minimizing the peak load associated with EVs in the distribution system. The second and third stages update their operation every five minutes, to fairly allocate the aggregated and individual EV loads in the MV and LV feeders, respectively, while minimizing active power losses and voltage deviations. The proposed technique is applied to CIGRE's North-American MV and LV benchmark systems to demonstrate its ability to properly allocate EV loads, and improve distribution system performance in terms of losses and voltage profiles.