Control and Bidding Strategy for Virtual Power Plants With Renewable Generation and Inelastic Demand in Electricity Markets

Zhao, Qianchuan; Shen, Ying; Li, Mingyang

doi:10.1109/tste.2015.2504561

Cited by 111 publications

(64 citation statements)

References 26 publications

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“…However, with dramatic development of WF and ESS, the scale of WF-ESS increases and consequently in some markets the WF-ESS may influence the cleared prices. Meanwhile, virtual power plant (VPP) can aggregate distributed WFs and ESS [6], [7], and take part in the competition in electricity markets as a large participant [8], [9]. Extending our former study on offering and operating strategies of WF-ESS as a price taker [1], this paper studies corresponding strategies of a large WF-ESS as a price maker in electricity markets, more specifically in energy markets.…”

Section: Introductionmentioning

confidence: 98%

Optimal Offering and Operating Strategy for a Large Wind-Storage System as a Price Maker

Ding

Pinson

et al. 2017

IEEE Trans. Power Syst.

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Abstract-Wind farms and energy storage systems are playing increasingly more important roles in power systems, which makes their offering non-negligible in some markets. From the perspective of wind farm-energy storage systems (WF-ESS), this paper proposes an integrated strategy of day-ahead offering and real-time operation policies to maximize their overall profit. As participants with large capacity in electricity markets can influence cleared prices by strategic offering, a large scaled WF-ESS is assumed to be a price maker in day-ahead markets. Correspondingly, the strategy considers influence of offering quantity on cleared day-ahead prices, and adopts linear decision rules as the real time control strategy. These allow enhancing overall profits from both day-ahead and balancing markets. The integrated price-maker strategy is formulated as a stochastic programming problem, where uncertainty of wind power generation and balancing prices are taken into account in the form of scenario sets, permitting to reformulate the optimization problem as a linear program. Case studies validate the effectiveness of the proposed strategy by highlighting and quantifying benefits comparing with the price-taker strategy, and also show the profit enhancement brought to the distributed resources.Index Terms-Electricity markets, energy storage system, linear decision rules, offering strategy, price-maker strategy, wind farm NOMENCLATURE Indices/Sets i ∈ I Index of wind farms. j ∈ J Index of energy storage systems t ∈ T Index of time intervals in a day.

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Section: Introductionmentioning

confidence: 98%

Optimal Offering and Operating Strategy for a Large Wind-Storage System as a Price Maker

Ding

Pinson

et al. 2017

IEEE Trans. Power Syst.

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“…The DSM undertaken by the active participation of end-user customers is a key aspect of a smart power system [11][12][13][14][15][16][17][18][19][20]. In optimized consumption, peak load demand clips, and the valleys load demand fill [5].…”

Section: Introductionmentioning

confidence: 99%

The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand

Mohammad

Mishra

2018

Energies

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This paper presents an interactive trading decision between an electricity market operator, generation companies (GenCos), and the aggregators having demand response (DR) capable loads. Decisions are made hierarchically. At the upper-level, an electricity market operator (EMO) aims to minimise generation supply cost considering a DR transaction cost, which is essentially the cost of load curtailment. A DR exchange operator aims to minimise this transaction cost upon receiving the DR offer from the multiple aggregators at the lower level. The solution at this level determines the optimal DR amount and the load curtailment price. The DR considers the end-user’s willingness to reduce demand. Lagrangian duality theory is used to solve the bi-level optimisation. The usefulness of the proposed market model is demonstrated on interconnection of the Pennsylvania-New Jersey-Maryland (PJM) 5-Bus benchmark power system model under several plausible cases. It is found that the peak electricity price and grid-wise operation expenses under this DR trading scheme are reduced.

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“…Studies on wind power bidding in the day-ahead EM with wind power penetration are too numerous to enumerate one by one. References [2][3][4] etc., for reasons such as low marginal cost of wind power producer (WPP) etc., hold that the bidding mode (BM) of a WPP is to only send the independent system operator (ISO) its power output plan for each period of the next day (namely, BM 1). The ISO ensures the wind power accommodation according to every WPP's day-ahead power output plan, but a WPP should be financially punished when its real time power output deviates from the day-ahead bidding one [3].…”

Section: Introductionmentioning

confidence: 99%

“…No matter under which BM, on the one hand, estimated profits of WPP 1 and WPP 2 in QDEMA 2 are higher than those in QDEMA 1, respectively, and estimated profits of WPP 3 , WPP 4 and WPP 5 in our proposed LSCAC-based approach are higher than those in QDEMA 2, respectively, which, to some extent, indicates one can get more profit by using the LSCAC algorithm to bid in EM than the Q-learning one within the same conditions; on the other hand, the operation cost in our proposed LSCAC-based approach is lower than that in QDEMA 2, and the operation cost in QDEMA 2 is lower than that in QDEMA 1, which, to some extent, indicates that with the increase in the number of LSCAC-based agents in EM, the operation cost of whole system can be reduced.…”

mentioning

confidence: 96%

Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing

Zhao¹,

Wang²,

Zhao³

et al. 2017

Energies

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Abstract:In this paper, considering real time wind power uncertainties, the strategic behaviors of wind power producers adopting two different bidding modes in day-ahead electricity market is modeled and experimentally compared. These two different bidding modes only provide a wind power output plan and a bidding curve consisting of bidding price and power output, respectively. On the one hand, to significantly improve wind power accommodation, a robust market clearing model is employed for day-ahead market clearing implemented by an independent system operator. On the other hand, since the Least Squares Continuous Actor-Critic algorithm is demonstrated as an effective method in dealing with Markov decision-making problems with continuous state and action sets, we propose the Least Squares Continuous Actor-Critic-based approaches to model and simulate the dynamic bidding interaction processes of many wind power producers adopting two different bidding modes in the day-head electricity market under robust market clearing conditions, respectively. Simulations are implemented on the IEEE 30-bus test system with five strategic wind power producers, which verify the rationality of our proposed approaches. Moreover, the quantitative analysis and comparisons conducted in our simulations put forward some suggestions about leading wind power producers to reasonably bid in market and bidding mode selections.

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Control and Bidding Strategy for Virtual Power Plants With Renewable Generation and Inelastic Demand in Electricity Markets

Cited by 111 publications

References 26 publications

Optimal Offering and Operating Strategy for a Large Wind-Storage System as a Price Maker

Optimal Offering and Operating Strategy for a Large Wind-Storage System as a Price Maker

The Role of Demand Response Aggregators and the Effect of GenCos Strategic Bidding on the Flexibility of Demand

Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing

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