A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

Emarati, Mohammadreza; Keynia, Farshid; Rashidinejad, Masoud

doi:10.1002/2050-7038.12255

Cited by 22 publications

(10 citation statements)

References 33 publications

(55 reference statements)

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“…Many research works are available with optimal scheduling of generation in the VPP network [44][45][46]. The grid integration of the VPP was presented with different approaches [54].…”

Section: Stochastic Adaptive Robust Optimization Methodsmentioning

confidence: 99%

Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework

et al. 2021

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Renewable energy sources prevail as a clean energy source and their penetration in the power sector is increasing day by day due to the growing concern for climate action. However, the intermittent nature of the renewable energy based-power generation questions the grid security, especially when the utilized source is solar radiation or wind flow. The intermittency of the renewable generation can be met by the integration of distributed energy resources. The virtual power plant (VPP) is a new concept which aggregates the capacities of various distributed energy resources, handles controllable and uncontrollable loads, integrates storage devices and empowers participation as an individual power plant in the electricity market. The VPP as an energy management system (EMS) should optimally dispatch the power to its consumers. This research work is proposed to analyze the optimal scheduling of generation in VPP for the day-ahead market framework using the beetle antenna search (BAS) algorithm under various scenarios. A case study is considered for this analysis in which the constituting energy resources include a photovoltaic solar panel (PV), micro-turbine (MT), wind turbine (WT), fuel cell (FC), battery energy storage system (BESS) and controllable loads. The real-time hourly load curves are considered in this work. Three different scenarios are considered for the optimal dispatch of generation in the VPP to analyze the performance of the proposed technique. The uncertainties of the solar irradiation and the wind speed are modeled using the beta distribution method and Weibull distribution method, respectively. The performance of the proposed method is compared with other evolutionary algorithms such as particle swarm optimization (PSO) and the genetic algorithm (GA). Among these above-mentioned algorithms, the proposed BAS algorithm shows the best scheduling with the minimum operating cost of generation.

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“…Many research works are available with optimal scheduling of generation in the VPP network [44][45][46]. The grid integration of the VPP was presented with different approaches [54].…”

Section: Stochastic Adaptive Robust Optimization Methodsmentioning

confidence: 99%

Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework

et al. 2021

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“…In literature [3], aiming at the operators of power selling companies in the spot market, the risk assessment algorithm, CVaR, was used to analyze power purchase strategies in medium-term and long-term markets and spot markets by taking into account renewable energy, distributed energy, rental ESD and other factors. In paper [4], the authors focused on a virtual power plant (VPP) in the power spot market. They conducted research on the quotation and operation optimization in the DA market and RT market and studied the impacts of market uncertainty on VPP operation by using the CVAR method.…”

Section: Prefacementioning

confidence: 99%

Operation Strategy of Electricity Retailers Based on Energy Storage System to Improve Comprehensive Profitability in China’s Electricity Spot Market

2021

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Due to the development of China’s electricity spot market, the peak-shifting operation modes of energy storage devices (ESD) are not able to adapt to real-time fluctuating electricity prices. The settlement mode of the spot market aggravates the negative impact of deviation assessments on the cost of electricity retailers. This article introduces the settlement rules of China’s power spot market. According to the electricity cost settlement process and the assessment methods, this paper proposes a comprehensive electricity cost optimization algorithm that optimizes day-ahead market (DA) electricity cost, real-time market (RT) electricity cost and deviation assessment through ESD control. According to the trial electricity price data of the power trading center in Guangdong province (China), many typical load curves and different deviation assessment policies, the algorithm calculates DA electricity cost, RT electricity cost and deviation assessment cost by utilizing a comprehensive electricity cost optimization algorithm. Compared with the original electricity cost and optimization cost, this method is proven to effectively save overall electricity costs under the spot market settlement system. Based on three different initial investment prices of ESD, this paper analyzes the economics of the ESD system and proves that ESD investment can be recovered within 5 years. Considering the small amounts of operating data in China’s power spot market, the algorithm generates random data according to characteristics of these data. Then, this paper verifies that the comprehensive electricity cost optimization algorithm remains reliable under random circumstances.

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“…The first step is collecting the relevant historical information, including electricity price, traffic condition and itineraries of drivers and riders, for various operating scenarios. After that, scenario generation methods, such as K‐means clustering, 31 are utilized to generate representative operating scenarios. Finally, the generated representative operating scenarios are used as the input to the proposed optimization model to derive the optimal solution.…”

Section: Problem Description and Assumptionmentioning

confidence: 99%

“…In the literature mentioned above, EVCS planning for EVs in car‐sharing business, considering all the vital aspects of routing selection, pickup/delivery of riders, along with stochastic modeling of traffic flows, riders' requirements, drivers' itineraries, and electricity prices is not fully addressed. Therefore, this paper develops a two‐stage stochastic programming framework 31,32 to address the uncertainties of traffic flows, riders' requirements, drivers' itineraries and electricity prices. Besides, risk management is adopted in this work.…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

Wang

Tang

Lai

et al. 2021

Int Trans Electr Energ Syst

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Summary With the increasing market share of electric vehicles (EVs), optimizing the planning decisions of charging stations becomes critical to support long‐distance traveling. Besides, the booming car‐sharing industry provides an alternative business model to enhance the utilization of vehicle resources. Recently, several car‐sharing enterprises have launched the collaborations with charging station construction companies to construct their own charging facilities, which calls for the co‐optimization of charging station planning and scheduling of EVs in car‐sharing business. Therefore, this paper develops an optimization model for electric vehicle charging stations placements and type selections, considering routing selection and charging management of an electric vehicle fleet in car‐sharing business. Specifically, a stochastic programming framework is exploited to incorporate a variety of operating scenarios, which reflect the uncertainties of riders' requirements, drivers' itineraries, traffic flow information, and electricity prices. The proposed model aims to improve the welfare of all riders and reduce total cost of fulfilling riders' pickup and delivery requirements simultaneously, in which determining the optimal locations and types of charging facilities is required. Moreover, portfolio management is integrated into the optimization model to mitigate investment risk. After a series of reformulation, the proposed optimization model can be recast into a mixed‐integer linear programming problem. Using two transportation networks, the effectiveness of the proposed model is validated in numerical studies. It finds that the optimal electric vehicle charging station planning strategy is determined according to the operator's preferences, electricity prices, and installation budgets. Moreover, diversity within operating scenarios yields the positive value‐of‐stochastic‐solution that demonstrates the necessity of implementing stochastic programming.

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A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

Cited by 22 publications

References 33 publications

Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework

Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework

Operation Strategy of Electricity Retailers Based on Energy Storage System to Improve Comprehensive Profitability in China’s Electricity Spot Market

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

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