Volt-VAR Control Through Joint Optimization of Capacitor Bank Switching, Renewable Energy, and Home Appliances

Tushar, Mosaddek Hossain Kamal; Assi, Chadi

doi:10.1109/tsg.2017.2648509

Cited by 56 publications

(22 citation statements)

References 23 publications

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“…Given the impacts of DERs on feeder voltages, researchers have worked actively on optimizing feeder voltages and reactive power (Volt-VAR optimization) by controlling and coordinating distribution system's legacy devices (capacitor banks and load tap changers (LTCs)) and new devices (smart inverters) [27][28][29][30]. The existing algorithms use centralized or distributed control approach to optimize the real-time operation of the feeder, while assuming that an advanced communication system exists to control and coordinate the feeder devices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The existing algorithms use centralized or distributed control approach to optimize the real-time operation of the feeder, while assuming that an advanced communication system exists to control and coordinate the feeder devices. For example, in [27] authors propose a non-cooperative game for Volt-VAR optimization (VVO) while achieving an optimal energy consumption. In [28], a mixed-integer quadratically constrained programming (MIQCP) problem is formulated to optimize VVO by controlling and coordinating capacitor banks, voltage regulators, and load-tap changers for day-ahead operation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It should be noted that this paper has a different objective when compared to the literature on feeder voltage control methods [27][28][29][30]. Our objective is to evaluate the relative benefits of legacy and new voltage control devices by comparing their different control modes.…”

Section: Objectives and Assumptionsmentioning

confidence: 99%

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Impacts of Voltage Control Methods on Distribution Circuit’s Photovoltaic (PV) Integration Limits

Dubey

2017

Inventions

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Abstract:The widespread integration of photovoltaic (PV) units may result in a number of operational issues for the utility distribution system. The advances in smart-grid technologies with better communication and control capabilities may help to mitigate these challenges. The objective of this paper is to evaluate multiple voltage control methods and compare their effectiveness in mitigating the impacts of high levels of PV penetrations on distribution system voltages. A Monte Carlo based stochastic analysis framework is used to evaluate the impacts of PV integration, with and without voltage control. Both snapshot power flow and time-series analysis are conducted for the feeder with varying levels of PV penetrations. The methods are compared for their impacts on (1) the feeder's PV hosting capacity; (2) the number of voltage violations and the magnitude of the largest bus voltage; (3) the net reactive power demand from the substation; and (4) the number of switching operations of feeder's legacy voltage support devices i.e., capacitor banks and load tap changers (LTCs). The simulation results show that voltage control help in mitigating overvoltage concerns and increasing the feeder's hosting capacity. Although, the legacy control solves the voltage concerns for primary feeders, a smart inverter control is required to mitigate both primary and secondary feeder voltage regulation issues. The smart inverter control, however, increases the feeder's reactive power demand and the number of LTC and capacitor switching operations. For the 34.5-kV test circuit, it is observed that the reactive power demand increases from 0 to 6.8 MVAR on enabling Volt-VAR control for PV inverters. The total number of capacitor and LTC operations over a 1-year period also increases from 455 operations to 1991 operations with Volt-VAR control mode. It is also demonstrated that by simply changing the control mode of capacitor banks, a significant reduction in the unnecessary switching operations for the capacitor banks is observed.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Impacts of Voltage Control Methods on Distribution Circuit’s Photovoltaic (PV) Integration Limits

Dubey

2017

Inventions

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“…The coordinated control problem is formulated as a mixed-integer second-order conic programming problem. The VVC problem is formulated as a non-cooperative mixed strategy game [8], which considers flexible loads, electric vehicles, and renewable energy sources. The limit on the number of switching operations of voltage regulating devices is considered in the VVC [9], which is formulated as a mixedinteger nonlinear programming problem.…”

Section: Introductionmentioning

confidence: 99%

Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems

Wang

Gao

et al. 2020

IEEE Trans. Smart Grid

225

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Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.

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“…Generally, RES has inherent variability and uncertainties that will affect the total energy production planning which is common among renewable energy and distributed energy [30]. Nevertheless, the deployment of RES has brought extensive changes to the present electrical power grid system [31], for making it a reliable and consumer-oriented system. This deployment of RES exhibits uncertainty that depend upon the share of input.…”

Section: Resmentioning

confidence: 99%

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Dahunsi

Javaid

et al. 2018

Energies

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Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.

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Volt-VAR Control Through Joint Optimization of Capacitor Bank Switching, Renewable Energy, and Home Appliances

Cited by 56 publications

References 23 publications

Impacts of Voltage Control Methods on Distribution Circuit’s Photovoltaic (PV) Integration Limits

Impacts of Voltage Control Methods on Distribution Circuit’s Photovoltaic (PV) Integration Limits

Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems

An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

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