Optimal Wind Bidding Strategies in Day-Ahead Markets

This paper presents a computer application for wind energy bidding in a day-ahead electricity market to better accommodate the variability of the energy source. The computer application is based in a stochastic linear mathematical programming problem. The goal is to obtain the optimal bidding strategy in order to maximize the revenue. Electricity prices and financial penalties for shortfall or surplus energy deliver are modeled. Finally, conclusions are drawn from an illustrative case study, using data from the day-ahead electricity market of the Iberian Peninsula

Deep Reinforcement Learning for Wind and Energy Storage Coordination in Wholesale Energy and Ancillary Service Markets

Global power systems are increasingly reliant on wind energy as a mitigation strategy for climate change. However, the variability of wind energy causes system reliability to erode, resulting in the wind being curtailed and, ultimately, leading to substantial economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) that serve as onsite backup sources. Yet, this auxiliary role may significantly hamper the BESS's capacity to generate revenues from the electricity market, particularly in conducting energy arbitrage in the Spot market and providing frequency control ancillary services (FCAS) in the FCAS markets. Ideal BESS scheduling should effectively balance the BESS's role in absorbing onsite wind curtailment and trading in the electricity market, but it is difficult in practice because of the underlying coordination complexity and the stochastic nature of energy prices and wind generation. In this study, we investigate the bidding strategy of a wind-battery system co-located and participating simultaneously in both the Spot and Regulation FCAS markets. We propose a deep reinforcement learning (DRL)-based approach that decouples the market participation of the wind-battery system into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while simultaneously bidding in the wholesale Spot and FCAS markets to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy for the wind-battery system and find that our strategy generates significantly higher revenue and responds better to wind curtailment compared to an optimization-based benchmark. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to individual market participation

Replacement Reserve for the Italian Power System and Electricity Market

Over the last years, power systems around the globe experienced deep changes in their operation, mainly induced by the widespread of Intermittent Renewable Energy Sources (IRES). These changes involved a review of market and operational rules, in the direction of a stronger integration. At European level, this integration is in progress, driven by the new European guidelines and network codes, which deal with multiple issues, from market design to operational security. In this framework, the project TERRE (Trans European Replacement Reserve Exchange) is aimed at the realization of a European central platform, called LIBRA, for the exchange of balancing resources and, in particular, for the activation of the procured Replacement Reserve (RR) resources. The Italian Transmission System Operator (TSO), TERNA, is a participant of the project and it is testing new methodologies for the sizing of RR and its required activation throughout the TERRE process. The aim of the new methodologies is to find areas of potential improvement in the sizing of RR requirements and activation, which open up the possibility for a reduction of the procurement cost, without endangering the security of the power system. This paper describes a new RR sizing methodology, proposed by TERNA, which is based on a persistence method, showing its results on real data and highlighting key advantages and potential limitations of this approach. In order to overcome these limitations, a literature review on alternative approaches has been carried out, identifying nowcasting techniques as a relevant alternative for the very short term forecast horizon. These one could be further investigated and tested in the future, using the proposed persistence method as a benchmark

A generic stochastic network flow formulation for production optimization of district heating systems

District heating is an important component in the EU strategy to reach the set emission goals, since it allows an efficient supply of heat while using the advantages of sector coupling between different energy carriers such as power, heat, gas and biomass. Most district heating systems use several different types of units to produce heat for hundreds or thousands of households. The technologies reach from natural gas-fired and electric boilers to biomass-fired units as well as waste heat from industrial processes and solar thermal units. Furthermore, combined heat and power units (CHP) units are often included to use the synergy effects of excess heat from electricity production. We propose a generic mathematical formulation for the operational production optimization in district heating systems. The generality of the model allows it to be used for most district heating systems although they might use different combinations of technologies in different system layouts. The mathematical formulation is based on stochastic programming to account for the uncertainty of production from non-dispatchable units such as waste heat and solar heat. Furthermore, the model is easily adaptable to different application cases in district heating such as operational planning, bidding to electricity markets and long-term evaluation. We present results from three real cases in Denmark with different requirements

Reinforcement learning for optimization of energy trading strategy

An increasing part of energy is produced from renewable sources by a large number of small producers. The efficiency of these sources is volatile and, to some extent, random, exacerbating the energy market balance problem. In many countries, that balancing is performed on day-ahead (DA) energy markets. In this paper, we consider automated trading on a DA energy market by a medium size prosumer. We model this activity as a Markov Decision Process and formalize a framework in which a ready-to-use strategy can be optimized with real-life data. We synthesize parametric trading strategies and optimize them with an evolutionary algorithm. We also use state-of-the-art reinforcement learning algorithms to optimize a black-box trading strategy fed with available information from the environment that can impact future prices

Enhancing resilience by reducing critical load loss via an emergent trading framework considering possible resources isolation under typhoon

Leveraging distributed resources to enhance distribution network (DN) resilience is an effective measure in response to natural disasters. However, the willingness and economy of distributed resources are typically ignored. To address this issue, this paper proposes an emergent trading framework that uses parking lots (PLs) as resources to provide power support to critical loads (CLs) in a blackout due to typhoons. In this trading framework, an evolutionary Stackelberg game-based trading model is established to consider maximizing all stakeholders' economic benefits, considering possible resources isolation under typical fault scenarios caused by typhoons, and a benefit allocation mechanism is proposed for all stakeholders to motivate all stakeholders to participate in the trading. This framework allows that critical loads could reduce their load loss, parking lots could receive adequate compensation to stimulate them to participate in the trading, and distribution utility could ensure its economic benefits. Furthermore, an iterative evolutionary-Stackelberg solution set-up is applied to obtain the equilibria of the proposed framework. Simulation results on the modified IEEE 69-bus test system and IEEE 123-bus test system reveal the validity of the proposed method