Demand Response Application by Strategic Wind Power Producers

This paper presents an optimal bidding strategy for a strategic wind power producer (WPP) in a distribution-level energy market (DLEM). The behavior of the WPP is modelled through a bi-level stochastic optimization problem where the upper-level problem maximizes the profit of the WPP and the lower-level problem describes the clearing processes of the DLEM while considering network constraints. The bi-level problem is a stochastic mathematical program with equilibrium constraints (MPEC) that is formulated as a mixed-integer linear programming (MILP) problem. The main focus of this study is investigating prosumers’ impact on the market power of the strategic WPP in a DLEM structure. In this model, the effect of flexible prosumers from the aspects of demand response (DR) participants and photovoltaic penetration level (PVPL) on the WPP’s offering strategy is investigated. Moreover, the impact of bilateral contract on the market power of the strategic WPP and the cleared prices of the network is addressed. The proposed model is implemented in an IEEE 33-bus and numerical results illustrate how behavior of flexible prosumers and PVPL index affect the decision making of the strategic WPP when network constraints are considered. Numerical results show that by active participation of prosumers in DR programs, the reliance of DLEM on the strategic WPP reduces. Moreover, if the WPP participates in bilateral contracts, its offering to the DELM decreases, and as the result, the cleared prices augment indicating market power of the WPP.

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding, which is a kind of financial tool available in most electricity markets of the United States. In the proposed model, virtual bidding is used to improve the wind power producer's market power in the day-ahead (DA) market by trading at multiple buses, which are not limited to the locations of the wind units. The optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization model. The upper-level problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk (CVaR) for risk management. The lower-level problem represents the clearing process of the DA market. By using the Ka-rush-Kuhn-Tucker (KKT) conditions, duality theory, and big- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> method, the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints (MPEC) model and then a mixed-integer linear programming (MILP) model, which can be solved by existing commercial solvers. To reduce the computational cost of solving the proposed stochastic optimization model for large systems, a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding. Case studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual bidding. The impacts of the transmission limits, wind unit location, risk aversion parameters, wind power volatility, and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

In the deregulated electricity market, wind power producers (WPPs) are required to submit their generation profile in the day-ahead (DA) market. The consideration of uncertainties associated with price and wind power become crucial to facilitate decision making and risk hedging for WPPs. In this paper, a bidding strategy based on robust optimization is proposed under two trading floors, namely DA market and balancing market. This approach has merits on modelling multiple uncertainties. In addition, the original nonconvex bi-level problem is reformulated into a LP master problem and a MILP subproblem by employing decomposition and linearization techniques. Case studies based on Nodic market demonstrate the effectiveness of the proposed approach.

In this article, a stochastic decision-making framework is presented in which a wind power producer (WPP) provides some required reserve capacity from demand response aggregators (DRAs) in a peer-to-peer (P2P) structure. In this structure, each DRA is able to choose the most competitive WPP, and purchase energy and sell reserve capacity to that WPP under a bilateral contract-based P2P electricity trading mechanism. Based on this structure, the WPP can determine the optimal buying reserve from DRAs to offset part of wind power deviation. The proposed framework is formulated as a bilevel stochastic model in which the upper level maximizes the WPP's profit based on the optimal bidding in the day-ahead and balancing markets, whereas the lower level minimizes DRAs' costs. In order to incorporate the risk associated with the WPP's decisions and to assess the effect of scheduling reserves on the profit variability, conditional value at risk is employed.

Wind power offer strategy in day-ahead market considering price bidding strategy for electric vehicle aggregators

An MPEC model for Strategic Offers in a Jointly Cleared Energy and Reserve Market under Stochastic Production

At present, wind power producers (WPP) are paid following feed-in tariffs in Belgium. This system will come to an end soon due to its high cost and the producers will have to bid in the day-ahead market. As wind owners cannot forecast their production perfectly, they will face imbalance costs or revenues. Imbalance price forecasting is therefore a critical problem. In this paper, we implement a machine learning model to assess the usefulness of introducing exogenous variables in imbalance price forecasting. This method shows improved results compared to classical methods. Since the imbalance price is obtained by the marginal cost of producing the missing energy, the strategic behaviour of a WPP will influence the imbalance price. In this paper, we propose a way to represent this influence as well as a formulation of a model to obtain the optimal bidding strategy in that situation. This model has been cast as a convex quadratic program that can readily be solved using a commercial solver.

In the electricity markets every deviation between the contracted power production and the real energy supplied is usually penalized or paid according to the market prices. This gives incentive to the market participants to provide accurate production schedules. Wind power producers (WPP) can generate production forecasts with limited accuracy due to wind nature itself. In the markets where WPP are treated as a balance responsible parties they will account for the day-ahead market prices and balancing energy prices when placing market bids in order to maximize their profit. This paper presents mixed integer optimization model for the optimal bidding of WPP production in the day-ahead market in order to minimize imbalance costs. The uncertainty related to the market prices on the day-ahead and balancing markets as well as WPP forecasts is accounted for by considering large scenario set which is than reduced by applying the fast forward scenario reduction algorithm.

European countries are increasing their share of power production from wind and solar energy to mitigate climate change. Also the relative contributions from PV and wind power production in Europe change over time. At present, the installed capacity of photovoltaic (PV) power for all of Europe is smaller than that of wind power with a ratio of 3:4. Future scenarios for the installations of PV and wind power capacities for 2050 suggest that this ratio will substantially change. Namely, the PV power capacity might exceed the wind power capacity with a ratio of 3:2 to 2:1. We test the hypothesis that the weather dependency of anomalies in the PV plus wind power production will change in the future compared to today. Specifically, we examine which synoptic weather patterns are associated with anomalies in the PV plus wind power production for the present and future installed capacities in Europe. To that end, we developed a renewable energy model for the installed capacity of 2019 and 2050. This model allows us to simulate hourly PV and wind power production at 6 km horizontal resolution for all of Europe. We analyze the weather dependency of power-production anomalies by pairing our model output with results of the classification of weather patterns from the German Weather Service. Our results highlight similar weather patterns associated with positive anomalies in the hourly PV plus wind power production for the 2019 and 2050 installation, namely weather patterns with prevailing westerly winds. However, weather patterns associated with negative anomalies strongly change between the two installations. We also assess the dependency of the results on the duration of the production anomalies. Particularly for long production anomalies, the associated weather patterns are different for the 2019 and 2050 installation. One exception is the weather pattern Anticyclonic Southeasterly that is associated with the lowest 10-day power production in Europe for both 2019 and 2050. Regionally, weather patterns have different impacts on different regions in Europe, when comparing the associated patterns between the 2019 and 2050 installation. For instance, anomalously low power production differ for the Iberian peninsula and Southeastern Europe when the two installed capacities are compared. Taken together, our study gives a systematic overview on changes in the weather dependency of anomalies in the mix of PV and wind power between 2019 and 2050.

This paper studies the effects of energy storage on market power in an imperfectly competitive day-ahead market. In the proposed model, the generation sector includes one dominant producer, a number of price-taking producers, and the energy storage that is considered to be price-taker. The dominant producer offers through a bi-level optimization problem. In the upper-level, the dominant producer maximizes its profit, subject to operational limits. The lower-level model includes the market clearing conditions. We convert this bi-level optimization problem into a mathematical program with equilibrium constraints (MPEC). Then the MPEC is recast as a mixed-integer linear programming problem using linearization techniques. Different case studies are provided to highlight some impacts of energy storage on market power, profit of dominant producer and social welfare in this market.

Increasing intermittent renewable energy sources (RESs) intensifies the imbalance between demand and generation, entailing the diversification of the deployment of electrical energy storage systems (ESSs). A large-scale biogas plant (LBP) installed with heating devices and biogas energy storage (BES) usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas, which is addressed in this paper. By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic, this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator (DRA) simultaneously. In this model, social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level. Compared with cases only with DRA, the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic (PV) generation up to 6.3&#x0025;, 18.1&#x0025;, and 18.9&#x0025; at 30&#x0025;, 40&#x0025;, and 50&#x0025; PV penetration levels, respectively, with a better economic performance. This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints (MPEC) using Karush-Kuhn-Tucker (KKT) conditions and solved by the Cplex solver. The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.

This paper concentrates on improving the convergence properties of the relaxation schemes introduced by Kadrani et al. and Kanzow and Schwartz for mathematical program with equilibrium constraints (MPEC) by weakening the original constraint qualifications. It has been known that MPEC relaxed constant positive-linear dependence (MPEC-RCPLD) is a class of extremely weak constraint qualifications for MPEC, which can be strictly implied by MPEC relaxed constant rank constraint qualification (MPEC-RCRCQ) and MPEC relaxed constant positive-linear dependence (MPEC-rCPLD), of course also by the MPEC constant positive-linear dependence (MPEC-CPLD). We show that any accumulation point of stationary points of these two approximation problems is M-stationarity under the MPEC-RCPLD constraint qualification, and further show that the accumulation point can even be S-stationarity coupled with the asymptotically weak nondegeneracy condition.

Wind power is one of the most rapidly growing clean and renewable energy sources. However, due to the uncertainty and intermittency of wind power, the increasing penetration of wind power into the electric power system will pose challenges to power system operators. Moreover, as a participant in a competitive electricity market, a wind power producer's behavior and profit will be influenced by other participants' behaviors. This paper proposes a model of using stochastic programming to generate optimal bidding strategies to maximize the total profits of wind and conventional power producers in both the energy market and a bilateral reserve market, where the reserve price is settled between wind and conventional power producers by using game theory. Case studies using real-world data for games in an electricity market with different types of players are performed to show the effectiveness of the proposed model.

Wind power represents a significant percentage of the Spanish generation mix and this trend will increase due to the commitment of the European Union to the full deployment of Directive 2009/28/EC. The increasing penetration of intermittent renewable energy, and the development of advanced information and communication technologies, give rise to questions on how additional flexibility obtained from loads can be used in order to optimize the use of resources and assets. This paper proposes a comprehensive stochastic decision making model for the coordinated operation of wind power producers and demand response (DR) aggregators participating in the day-ahead market. In order to account for the uncertainty around the true outcomes of day-ahead prices and wind power, a minimum conditional value at risk term has been included in the model formulation. Numerical results illustrate how the proposed bidding strategy for wind and demand response (DR) pairing increases the expected benefit of both resources and reduces the related risk.

On the marginal value of electricity storage