Second-Order Stochastic Dominance Constraints for Risk Management of a Wind Power Producer's Optimal Bidding Strategy

AlAshery, Mohamed Kareem; Xiao, Dongliang; Wang, Qiao

doi:10.1109/tste.2019.2927119

Cited by 56 publications

(36 citation statements)

References 32 publications

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“…Thus, the extreme scenario in the generated sample set can be ignored for achieving economic PV hosting capacity. To simulate the PV curtailment, we adopt the chance-constrained programming and build a chance-constrained PV hosting capacity assessment model by replacing the deterministic power balance constraints (11) and (12) with the joint chance constraint (18).…”

Section: B Chance-constrained Socp Modelmentioning

confidence: 99%

“…where δ is the curtailment probability. The joint chance constraint (18) guarantees that the constraint violation probability is less than a predefined risk level, i.e., the power balance constraints ( 11) and ( 12) will not exceed in (1-δ)´100% scenarios. The risk level δ provides a natural and direct way to reflect the risk preferences of the DNO.…”

Section: B Chance-constrained Socp Modelmentioning

confidence: 99%

“…Since inflexible operation rules usually lead to an uneconomic decision in planning, some works try to balance the DG curtailment risk and investment cost. For instance, [17] and [18] employ the conditional value at risk (CVaR) and stochastic dominance technique to gain a more efficient planning result.…”

Section: Introductionmentioning

confidence: 99%

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Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Yuan

Zhu

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Successful distributed photovoltaic (PV) planning now requires a hosting capacity assessment process that accounts for an appropriate model of PV output and its uncertainty. This paper explores how the PV hosting capacity of distribution networks can be increased by means of spatial correlation among distributed PV outputs. To achieve this, a novel PV hosting capacity assessment method is proposed to account for arbitrary geographically dispersed distributed PVs. In this method, the empirical relation between the spatial correlation coefficient and distance is fitted by historical data in one place and then applied to model the joint probability distribution of PV outputs at a neighboring location. To derive the PV hosting capacity at candidate locations, a stochastic PV hosting capacity assessment model that aims to maximize the PV hosting capacity under thermal and voltage constraints is proposed. Benders decomposition algorithm is also employed to reduce the computational cost associated with the numerous sampling scenarios. Finally, a rural 59-bus distribution network in Suzhou, China, is used to demonstrate the effectiveness of the proposed PV hosting capacity assessment methodology and the significant benefits obtained by increasing geographical distance.

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Section: B Chance-constrained Socp Modelmentioning

confidence: 99%

Section: B Chance-constrained Socp Modelmentioning

confidence: 99%

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Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Yuan

Zhu

et al. 2021

Journal of Modern Power Systems and Clean Energy

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“…In the relative stable environment which can be mathematically expressed, the traditional optimization problem can be optimized by many different ways to get the optimal solution. M.K.AlAshery et al established stochastic models to ensure that programming for the risk of the selected objective function distribution does not exceed a certain limit [26]. Bilevel optimization method is usually a good choice when faced with a comprehensive solution of multilevel or multi-party interests [27], [28].…”

Section: Figure 1 Rps and Tgc Mechanisms In Chinamentioning

confidence: 99%

Joint Optimization of Quota Policy Design and Electric Market Behavior Based on Renewable Portfolio Standard in China

Liu

Wang

et al. 2021

IEEE Access

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Under the perspective of carbon neutrality, the green electricity absorption target constrained by the quota system policy plays a crucial role in reducing the carbon emission of the power industry. However, the current green certificate policy has not achieved good results. On the premise of reducing the additional market burden as much as possible, the policy parameters should take into account the influence of market behavior to formulate better policy parameters in line with China's carbon emission peak goal. This paper constructs a combined hierarchical reinforcement learning with off-policy correction and multiagent deep deterministic policy gradient algorithm (HIRO-MADDPG). It realizes the benefit analysis of the existing policy parameters joint with the solution of the optimal policy parameters. The algorithm solves the problem that benefit analysis and parameter formulation cannot be jointly trained and improves the precision. The results indicate:(1)HIRO-MADDPG algorithm can reach the highest policy benefits on the premise of maintaining market fairness.(2)Under the new optimal policy parameters, the income per kilowatt hour of thermal power generator(TPG) and renewable power generator(RPG) can be maintained at 10% under the condition of abolishing subsidies.(3)With the help of the new policy parameters, China's power sector will reach the peak of carbon emissions from coal-fired power plants in 2026 ahead of schedule, and reduce carbon emissions by a further 11% by 2030.

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“…As performed in [7]- [9], the most commonly used risk measure is Conditional Value at Risk (CVAR), which is added to the objective function (expected profit) multiplied by a weighting factor, representing the risk adversity of the WPP. In [10], stochastic dominance constraints are included in optimisation problems to manage the negative tail of profit distribution, which outperforms the CVAR method. Moreover, risk assessment of distribution networks due to adverse weather condition is conducted in [11], where associated warnings are provided as the appropriate storage or trading signals for prosumers with renewable sources.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Market Trading Strategy to Hedge Financial Risks of Wind Power Producers in Electricity Markets

Shamsi

Cuffe

2021

IEEE Trans. Power Syst.

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Second-Order Stochastic Dominance Constraints for Risk Management of a Wind Power Producer's Optimal Bidding Strategy

Cited by 56 publications

References 32 publications

Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Joint Optimization of Quota Policy Design and Electric Market Behavior Based on Renewable Portfolio Standard in China

A Prediction Market Trading Strategy to Hedge Financial Risks of Wind Power Producers in Electricity Markets

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