Day-ahead and Intraday Dispatch of an Integrated Biomass-Concentrated Solar System: A Multi-Objective Risk-Controlling Approach

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An Innovative Coalitional Trading Model for a Biomass Power Plant Paired with Green Energy Resources

Brunel Research Initiative and Enterprise Fund (BRIEF) Optimal distribution network investment and distribution network operator dispatch under peer-to-peer market

Offering Strategy of Thermal-Photovoltaic-Storage Based Generation Company in Day-Ahead Market

Designing appropriate strategies for the participation of generation companies (GenCos) in the electricity markets has always been a concern for researchers. Generally, a set of dispatchable and non-dispatchable units constitute GenCos. This chapter presents a coordinated offering structure for the participation of a GenCo consisting of thermal, photovoltaic (PV), and battery storage system (BSS) in the day-ahead (DA) electricity market. The proposed offering structure is formulated as a three-stage stochastic programming problem while a scenario-based technique is utilized to handle the uncertainty related to electricity prices and PV production. From another point of view, a compatible risk-measuring index with multi-stage stochastic programming problems, namely conditional value at risk (CVaR), is also considered in the proposed structure. The proposed offering model is not only able to derive the offering curves of GenCo but also is capable of applying various emission limitations pertaining to thermal units.©2020 Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Nojavan, S., & Zare K. (eds). Electricity markets : new players and pricing uncertainties. Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-36979-8_6.fi=vertaisarvioitu|en=peerReviewed

Risk-based probabilistic-possibilistic self-scheduling considering high-impact low-probability events uncertainty

In recent years, examining the ruinous consequence of extreme weather events on the power system is one of the most challenging issues that researchers have confronted to. Considerable extreme conditions are generally the missing part of a realistic self-scheduling problem. Considering high-impact low-probability (HILP) events into the model have at least two benefits: first, generation companies (GenCos) can elude from financial disadvantages of upcoming HILP events and then the ISO can better clear energy and reserve markets with a preventive-oriented process to enhance power system resilience. This paper provides a pre-extreme condition self-scheduling for a price-taker generation company with renewable generation units which participates in the day-ahead energy and spinning reserve markets. Uncertainties associated with electricity prices and wind power production are characterized by multiple stochastic scenarios. The stochastic behavior of wind power is presented by using the Beta probability density function (PDF). In order to model the uncertainty of forced outages of generating units due to HILP events and the probability of being called for reserve deployment, a possibilistic approach is proposed. By comparing the generation scheduling under different risk factors and according to the financial disadvantages of HILP events, the conditional value-at-risk (CVaR) risk-averse strategy is considered into the model

Risk-involved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model

In this paper, a hybrid power generation company consisting of a concentrated solar power unit, wind turbines, a battery system, and a demand response provider is established to take part in electricity markets. The operating strategy of the hybrid power generation company in day-ahead and adjustment (intraday) markets is determined based on their coordinated operation. To tackle the intrinsic uncertainties, for the first time, a mixed stochastic-interval model is proposed which addresses the uncertainty in demand response and solar energy via interval optimization. The examined problem is formulated as a multi-objective optimization problem in which the risk of both stochastic and interval parameters can be involved. On this basis, the proposed operating strategy covers three objective functions, namely, expected radius and midpoint of the hybrid power generation company's profit together with the conditional value-at-risk. Accordingly, the normal boundary intersection and lexicographic optimization techniques are utilized to derive feasible solutions. Lastly, numerical results are presented and the performance of the proposed framework is investigated. The results indicate that the suggested model can be efficiently used to handle the decision-maker's preference over interval and stochastic parameters, and the risk criterion associated with interval parameters becomes larger as the forecasting errors increase

Offering and bidding for a wind producer paired with battery and CAES units considering battery degradation

This paper presents a stochastic framework for offering and bidding strategies of a hybrid power generation system (HPGS) with a wind farm and two types of energy storage facilities, i.e., compressed air energy storage (CAES) and battery energy storage (BES) systems. The model considers the participation of the HPGS in consecutive electricity markets, i.e., day-ahead (DA) and intraday markets. To better address the proposed trading strategy problem, the BES degradation cost is also incorporated into the model. Furthermore, a mechanism based on energy procurement from demand response resources (DRRs) in the intraday demand response exchange (IDREX) market for the HPGS is also established to offset unexpected energy imbalances effectively. The suggested offering and bidding strategy is formulated as a three-stage stochastic programming problem incorporating a risk-alleviating index, namely, the conditional value-at-risk (CVaR). Results from several simulations indicate considerable profit gain and risk reduction achieved by the suggested offering and bidding framework

Offering strategy of thermal-photovoltaic-storage based generation company in day-ahead market

Designing appropriate strategies for the participation of generation companies (GenCos) in the electricity markets has always been a concern for researchers. Generally, a set of dispatchable and non-dispatchable units constitute GenCos. This chapter presents a coordinated offering structure for the participation of a GenCo consisting of thermal, photovoltaic (PV), and battery storage system (BSS) in the day-ahead (DA) electricity market. The proposed offering structure is formulated as a three-stage stochastic programming problem while a scenario-based technique is utilized to handle the uncertainty related to electricity prices and PV production. From another point of view, a compatible risk-measuring index with multi-stage stochastic programming problems, namely conditional value at risk (CVaR), is also considered in the proposed structure. The proposed offering model is not only able to derive the offering curves of GenCo but also is capable of applying various emission limitations pertaining to thermal units

Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model

Renewable energy resources such as wind, either individually or integrated with other resources, are widely considered in different power system studies, especially self-scheduling and offering strategy problems. In the current paper, a three-stage stochastic multi-objective offering framework based on mixed-integer programming formulation for a wind-thermal-energy storage generation company in the energy and spinning reserve markets is proposed. The commitment decisions of dispatchable energy sources, the offering curves of the generation company in the energy and spinning reserve markets, and dealing with energy deviations in the balancing market are the decisions of the proposed three-stage offering strategy problem, respectively. In the suggested methodology, the participation model of the energy storage system in the spinning reserve market extends to both charging and discharging modes. The proposed framework concurrently maximizes generation company's expected profit and minimizes the expected emission of thermal units applying lexicographic optimization and hybrid augmented-weighted ∊-constraint method. In this regard, the uncertainties associated with imbalance prices and wind power output as well as day-ahead energy and spinning reserve market prices are modeled via a set of scenarios. Eventually, two different strategies, i.e., a preference-based approach and emission trading pattern, are utilized to select the most favored solution among Pareto optimal solutions. Numerical results reveal that taking advantage of spinning reserve market alongside with energy market will substantially increase the profitability of the generation company. Also, the results disclose that spinning reserve market is more lucrative than the energy market for the energy storage system in the offering strategy structure