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Bi-Level Volt-VAR Optimization to Coordinate Smart Inverters with Voltage Control Devices
摘要： Conservation voltage reduction (CVR) uses Volt-VAR optimization (VVO) methods to reduce customer power demand by controlling feeder's voltage control devices. The objective of this paper is to present a VVO approach that controls system's legacy voltage control devices and coordinates their operation with smart inverter control. An optimal power flow (OPF) formulation is proposed by developing linear and nonlinear power flow approximations for a three-phase unbalanced electric power distribution system. A bi-level VVO approach is proposed where, Level-1 optimizes the control of legacy devices and smart inverters using a linear approximate three-phase power flow. In Level-2, the control parameters for smart inverters are adjusted to obtain an optimal and feasible solution by solving the approximate nonlinear OPF model. Level-1 is modeled as a Mixed Integer Linear Program (MILP) while Level-2 as a Nonlinear Program (NLP) with linear objective and quadratic constraints. The proposed approach is validated using 13-bus and 123-bus three-phase IEEE test feeders and a 329-bus three-phase PNNL taxonomy feeder. The results demonstrate the applicability of the framework in achieving the CVR objective. It is demonstrated that the proposed coordinated control approach help reduce feeder's power demand by reducing the bus voltages; the proposed approach maintains an average feeder voltage of 0.96 pu. A higher energy saving is reported during the minimum load conditions. The results and approximation steps are thoroughly validated using OpenDSS.
关键词： three-phase optimal power flow,Volt-VAR optimization,distributed generators,smart inverters
更新于2025-09-23 15:23:52
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Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization
摘要： The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back-up unit (storage). Uncertain solar irradiance and wind speed are simulated via log-normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed-rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm.
关键词： power system planning,optimal power flow,solar energy,wind energy,optimization
更新于2025-09-12 10:27:22
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[IEEE 2019 IEEE Industry Applications Society Annual Meeting - Baltimore, MD, USA (2019.9.29-2019.10.3)] 2019 IEEE Industry Applications Society Annual Meeting - Multi-time scale coordinated scheduling for the combined system of wind power, photovoltaic, thermal generator, hydro pumped storage and batteries
摘要： Grid connection of random renewable energy such as wind power and photovoltaic results in difficulties of keeping power balance for power system operation. In order to solve this problem, this paper proposed a multi-time scale coordinated scheduling model for the combined system of Wind power-Photovoltaic-Thermal generator-Hydro pumped storage-Battery (WPTHB) by taking advantages of their complementary operation characteristics. The scheduling model is composed of three time scales: the day-ahead scheduling, the 1-hour ahead scheduling and 15-minute ahead scheduling. 1) in the day-ahead scheduling, based on the day-ahead forecast data of Wind-Photovoltaic power and Load demand (WPL), the optimal power outputs of thermal power units in 24 hours are solved from a mix integer linear programing (MILP) model to achieve the minimal operation cost of thermal units. 2) In the 1-hour ahead scheduling, based on power outputs of thermal units solved in the day-ahead scheduling and the 1-hour-ahead forecasted WPL, the hydro pumped storage power output is optimized to achieve its minimal operation cost. 3) In the 15-minute ahead scheduling, based on the day-ahead optimal power outputs of thermal units and the 1-hour ahead optimal outputs of pumped storages, the battery optimal power generation is obtained from a AC optimal power flow model solved by MATPOWER. Simulations of New England system validate that the proposed multi-time scale coordinated scheduling model could fully explore the different power regulation speeds and capacity of hydro pumped storages, thermal power generators and batteries to effectively alleviate WPL variations and achieve economic operation for multi-source generation systems.
关键词： day-ahead scheduling,mix integer linear programing,AC-optimal power flow,15-minute ahead scheduling,1-hour ahead scheduling,coordinated scheduling model,Multi-time scale
更新于2025-09-12 10:27:22
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Power Management in Active Distribution Systems Penetrated by Photovoltaic Inverters: A Data–Driven Robust Approach
摘要： Under the smart grid paradigm, distribution systems with large penetrations of photovoltaic–based power generation are called to optimize their operational resources to achieve a more ef?cient and reliable performance. In this context, this paper proposes a multiperiod mixed integer second order cone formulation to optimize distribution feeders operation. The model takes into account the feeder physical behavior; discrete control equipment (tap changers and capacitors banks) with a maximum allowable daily switching operation number; photovoltaic inverters operation; and the uncertain nature of solar energy and loads. A two–stage robust optimization framework is used to include the uncertainty into the model, where discrete and continuous control actions are assumed to be part of the ?rst and second stage of this model, respectively. The conservativeness level of the robust model is controlled by an polyhedral uncertainty set whose vertexes are adaptively adjusted in a data–driven fashion in order to better capture complex spatiotemporal dependencies among uncertain parameters. Extensive computational experiments are performed utilizing modi?ed versions of various IEEE test feeders. The performance of the proposed data–driven model is contrasted against traditional deterministic and robust budget–constrained models, using a rolling horizon out–of–sample evaluation methodology. When compared to the deterministic model, the data–driven approach yields a reduction in power losses of approximately 15% and a reduction up to 98% in hourly voltage violations. Results also suggests that the proposed approach exhibits better performance in terms of both average and conditional–value–at–risk metrics in comparison to budget–constrained models.
关键词： Distribution Systems,Data-Driven Optimization,Optimal Power Flow,Robust Optimization,Volt/VAR Control
更新于2025-09-12 10:27:22
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[IEEE 2019 IEEE Milan PowerTech - Milan, Italy (2019.6.23-2019.6.27)] 2019 IEEE Milan PowerTech - optimising Load Flexibility for the Day Ahead in Distribution Networks with Photovoltaics
摘要： In this paper a methodology is proposed to calculate the load demand ?exibility that could be activated within the next 24-hours for solving the technical impacts of contingencies that may come up in an unbalanced low voltage distribution networks with high penetration of intermittent DG sources. The methodology is formulated within a Demand Response program environment via load shifting as ?exibility enabler mechanism. To achieve that, a non-linear optimisation problem is formulated based on an unbalanced optimal power ?ow, which allows the determination of the load ?exibility that each Demand Response customer could provide at the request of the Distribution System Operator. The demand as well as weather conditions are forecasted for the day ahead. The optimisation problem is solved in a sequence fashion, within a daily framework, splitting the whole problem in optimisation blocks. In each block, the ?exible load demand is obtained and the load demand forecasting its updated for the upcoming blocks based on the changes in the scheduled load demand. The methodology is applied to a real distribution network with the load data received from the smart metering infrastructure. The results obtained show the strength of the methodology in solving the technical problems of the network under high unbalanced operation.
关键词： Photovoltaics,Demand Response,Load Flexibility,Distribution Networks,Optimal Power Flow
更新于2025-09-12 10:27:22
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[IEEE 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) - Waikoloa Village, HI (2018.6.10-2018.6.15)] 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) - Optimal Use of Distributed Resources to Control Energy Variances in Microgrids
摘要： This paper presents the optimization of distributed energy resources in a community microgrid. An optimal power flow is used to determine the optimal allocation of resources using an evolutionary programming method, achieving the lowest cost of supplying the demand while accounting for physical and operational constraints. The energy variances were managed and controlled within the microgrid. Different scenarios of high integration of distributed resources were studied using the algorithm. The utility could be used to supply a constant energy block or for backup power. The algorithm was successfully used to allocate resources while achieving a high load factor value.
关键词： Demand Response,Distributed Energy Resources,Optimal Power Flow,Microgrids,Photovoltaic Systems,Battery Storage Systems
更新于2025-09-11 14:15:04