Energy management for aggregate prosumers in a virtual power plant: A robust Stackelberg game approach

Yin, Shuangrui; Ai, Qian; Li, Zhaoyu; Zhang, Yufan; Lu, Tianguang

doi:10.1016/j.ijepes.2019.105605

Cited by 94 publications

(25 citation statements)

References 33 publications

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“…On the other side, there is a moving trend towards decentralized markets with distributed ledger and Blockchain technologies, eliminating the third party for improving security and transparency [17] and [18]. A P2P-ready consensus-based distributed algorithm that fully discards the role of the central controller and fully compatible to prosumer preferences, for economic operation of distribution grid is proposed in [19] and [20]. Authors in [17] - [22] viewed the P2P market from prosumers' perspective only, without considering operators' technical preferences.…”

Section: Microgrid Prosumer Consortiummentioning

confidence: 99%

“…That is, the scenarios in which is less than are considered to be 'risky'. To validate the risk in deviation of metered energy measurements from the committed transactions, the metrics , and are used as shown in equations (19), (20) and (21). These indices are derived from Mean Absolute Percentage Error (MAPE), Mean absolute Deviation (MAD) and Root Mean Square Error (RMSE) respectively.…”

Section: Metrics For Validating Energy Riskmentioning

confidence: 99%

See 1 more Smart Citation

Realistic Energy Commitments in Peer-to-Peer Transactive Market with Risk Adjusted Prosumer Welfare Maximization

Mohan¹,

Bu²,

Jisma³

et al. 2020

Preprint

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As the local energy sources are mostly uncertain and fluctuating in nature, the ‘energy risk’ due to discrepancies between committed energy transactions and metered measurements is prominent in peer to peer (P2P) markets. We propose a P2P market settlement mechanism which lowers this risk and maximizes the welfare of buyers and sellers. The risk in energy production is modeled using Markowitz portfolio theory and the best point where energy return per unit risk is maximum is obtained from the efficient frontier by using the modified Sharpe ratio. The energy portfolio thus obtained is used as a constraint while optimizing the conflicting prosumer benefits using multi-objective stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC-NSPSO). In effect, only a reliable proportion of total energy demand submitted in the bid is cleared in the market, foreseeing the real-time fluctuations. The proposed market settlement mechanism also gives room to the existing distribution system operators by assigning them the duty of 1) optimally allocating energy among buyers and sellers in accordance with their competitive bids 2) providing the infrastructure, managing the market and charging for the service and 3) checking the technical feasibility by performing load flow and monitoring power transfer sensitivities to encourage short distance transactions. The energy allocation is done in CIGRE LV benchmark microgrid with ten peers having solar and wind generation. The allocated energy is found to be closer to the metered measurements and hence the reserve cost is observed to be low.

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Section: Microgrid Prosumer Consortiummentioning

confidence: 99%

Section: Metrics For Validating Energy Riskmentioning

confidence: 99%

Realistic Energy Commitments in Peer-to-Peer Transactive Market with Risk Adjusted Prosumer Welfare Maximization

Mohan¹,

Bu²,

Jisma³

et al. 2020

Preprint

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“…Many studies have been researched on VPPs, applying technical optimization-based [8][9][10], meta-heuristic [11][12][13][14], game-theoretic [15][16][17], and deep-learning-based [18,19] approaches Appl. Sci.…”

Section: Introductionmentioning

confidence: 99%

“…Pal et al suggested the optimal scheduling of generation in VPPs for a day-ahead market framework using the beetle antenna search algorithm under various scenarios [14]. Yin et al proposed a two-stage, robust Stackelberg game considering the uncertainties of renewable energy output and market prices for optimal scheduling in a VPP [15]. Wang et al conducted a feasibility study of urban virtual power plants with the goal of energy self-sufficiency and applied Shapley value-based cooperative game theory, aiming to benefit both the plant and demand sides [16].…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities

2021

Applied Sciences

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This paper focuses on a virtual power plant (VPP) implementation strategy for smart local energy communities (SECs) with energy service providers. It is difficult to balance energy in the implementation stage due to uncertainties in demand and resources. Therefore, VPP implementation was modeled using the risk factor of energy balance. Using this risk factor, it was shown that the temporal correlation between demand and resources was the dominant factor involved in VPP implementation. Based on this, two risk-based VPP implementation strategies are proposed: an optimization-based strategy and a simple strategy that is solved in an iterative way. To minimize VPP implementation costs, the proposed strategies select the resources that have high correlation coefficients with demand and low correlation coefficients with other resources. Experimental results using real data sets show that the proposed strategies based on the risk factor are effective means of VPP implementation for commercial and residential SECs. The results imply that VPPs for commercial SECs are possible when PV is used as the main resource and is supplemented by wind, and it is effective to configure VPPs for residential SECs using wind according to the correlation between demand and resources.

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“…Robust optimization as a alternative approach for dealing with uncertainties, only requires a deterministic uncertainty set instead of accurate probability distribution [5]. In [6], a two-stage robust Stackelberg game is proposed to solve the problem of day-ahead (DA) energy management of a VPP, the uncertainties of intermittent renewable energy output and market price are modeled using uncertainty sets. In [7], the authors established the DA scheduling model of VPP.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Minimax Regret-Based Self-Scheduling Strategy for Virtual Power Plants

Wang

Jia

Lai

et al. 2021

2021 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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Energy management for aggregate prosumers in a virtual power plant: A robust Stackelberg game approach

Cited by 94 publications

References 33 publications

Realistic Energy Commitments in Peer-to-Peer Transactive Market with Risk Adjusted Prosumer Welfare Maximization

Realistic Energy Commitments in Peer-to-Peer Transactive Market with Risk Adjusted Prosumer Welfare Maximization

Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities

Two-Stage Minimax Regret-Based Self-Scheduling Strategy for Virtual Power Plants

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