Strategic Offering of a Price Maker Wind Power Producer in Distribution-Level Energy Markets in Presence of Flexible Prosumers

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.© 2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Evaluating the Impact of Bilateral Contracts on the Offering Strategy of a Price Maker Wind Power Producer

Due to the high penetration of wind power generation in power systems and electricity markets, wind power plants (WPPs) can, in some scenarios, influence the market prices and exercise market power in the day-ahead (DA) market. In order to evaluate the capability of WPPs to directly act as price-maker, this article proposes the strategic offering of a WPP in the DA market by using a bilevel stochastic optimization approach. The primary objective of the proposed model is to maximize the WPP's expected profit by strategically offering in DA market while minimizing the energy deviations in the regulating market. Moreover, the WPP can also sign bilateral contracts with customers to supply their required energy. In the subproblem, the system operator tends to minimize the sum of the total generation costs minus the sum of the total demand benefits. The effect of bilateral contracts on the strategic offering of WPP in the DA market and its impact on the transmission margin are also investigated. Results on real cases show that when the WPP enters into a bilateral contract, it should consider the effect of such contracts on the offering strategy to the DA market. The effects of bilateral contracts on the regulating market are also examined.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Investigating the Impact of External Demand Response Flexibility on the Market Power of Strategic Virtual Power Plant

In this paper, a decision-making framework is proposed for a virtual power plant (VPP) to participate in day-ahead (DA) and regulating market (RM) considering internal demand response (IDR) flexibility. In the proposed model, a DR exchange market (DRXM) is also introduced to cover deviations of uncertain resources and decrease VPP’s imbalance penalties in the RM. The VPP can optimize its procurement expenditures by providing DR services from both IDR providers and DRXM. A market inefficiency index (MII) is defined to analyze the effect of trading energy in the DRXM on the market power of the VPP. The proposed model is formulated as a bi-level problem, in which at the upper level, the VPP maximizes its profit while at the lower level, the distribution system operator (DSO) strives to clear both DA and RM markets to maximize social welfare. The proposed problem is nonlinear and converted into a linear single-level problem through Karush-Kuhn-Tucker (KKT) optimality conditions and duality theory. The simulation results show that in high external demand response (EDR) participants, the expected profit of the VPP augments about 3% which is a substantial value for the one-day scheduling horizon. Furthermore, by providing EDR services, MII reduces which implies the EDRs preserve their economic surplus.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Investigation on electricity market designs enabling demand response and wind generation

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a volatilidade das energias renováveis, em particular da energia eólica, acarretam vários desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios. Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável (RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As incertezas associadas à geração eólica são consideradas através de um modelo de programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE modificado para demonstrar o efeito da DR na redução do custo de operação

Market Power with Combined Heat and Power Production in the Nordic Energy System

The trend toward increasing energy efficiency and variable renewable energy (VRE) production has implications for combined heat and power (CHP) plants, which operate in both the price-driven power market and the district heating (DH) sector. Since CHP will be important in VRE integration, we develop a complementarity model to analyze CHP producers' roles in integrated markets. We use a Nordic case study to gain insights into (i) the effect of the link between CHP and DH on market power and (ii) market power's impact on operations in the DH sector. The results indicate that (i) the link of CHP to DH supply can increase market power and (ii) market power can induce shifts in DH production from heat-only to CHP

Systematic analysis of the evolution of electricity and carbon markets under deep decarbonization

The decarbonization of electricity generation presents policy makers in many countries with the delicate task of balancing initiatives for technological change with a commitment to market liberalization. Despite the theoretical attractions, it has become doubtful whether carbon markets by themselves can offer the desired solution. We address this question through an integrated modeling framework, stylized for the Great Britain (GB) power market within the EU ETS, which includes three distinct components: (a) long-term least-cost capacity planning, similar in functionality to many used in policy analysis, but innovative in providing the endogenous calculation of carbon prices; (b) short-term price risk analysis producing hourly dispatch and pricing outputs, which are used to test the annual financial performance metrics implied by the longer-term investments; and (c) agent-based computational learning to derive pricing behavior in imperfect markets. The results indicate that the risk/return profile of electricity markets may deteriorate substantially as a result of decarbonization, reducing the propensity of companies to invest in the absence of increased government support and/or more beneficial market circumstances. If allowed, markets may adjust by deferring investment until conditions improve, consolidating to increase market power, or operating in a tighter market with reduced spare capacity. To the extent that each of these “market-led” solutions may be politically unpalatable, policy design will need to sustain a delicate regulatory regime, moderating the possible increased market power of companies while maintaining low-carbon subsidies for longer than expected. This paper considers some of the modeling implications for this compromise

Cyber-Physical Systems Design: Electricity Markets and Network Security

This thesis presents Cyber-Physical Systems Design (CPS Design). Design of CPS is challenging and requires interdisciplinary studies of engineering and economics because of the distinguishing features of CPS: strategic (self profit-maximizing) decision makers, complex physical constraints, and large-scale networked systems. We study these features by focusing on designing markets with complex constraints including both policy and physical constraints, and decomposing large-scale CPS within the context of electricity markets and network security. We first study market design for implementation of complex electricity policy targets, i.e. sustainability, reliability, and price efficiency, by efficient design of spot, carbon, and capacity markets that correct the deficiencies of the current electricity markets; this design does not take into account the network constraints due to the Kirchhoff's laws. To address this problem, we develop a framework based on the design of efficient auctions with constraints. Our market design sheds light on major debates in electricity policy including capacity-and-energy vs energy-only markets, carbon market vs carbon tax, and use of price or offer caps. Second, we add network constraints due to Kirchhoff's laws of current and voltage, which are unique to electricity networks, to the design of electricity spot markets with complex physical constraints. To address this problem, we develop a framework for the design of networked markets based on the ideas from local public goods. Finally, we study the design of defense policies for large-scale network security. Our approach is to design approximately optimal defense policies that are computable. We develop a framework based on the notion of influence graph, which captures the connectivity of the security states of the system elements, to decompose the system into subsystems. We then design approximately optimal defense policies for each sub-system. We consider non-Bayesian uncertainty and even though we do not model the attacker as a strategic decision maker, we compensate (in part) for the lack of this feature by adopting a minmax performance criterion.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144165/1/rasouli_1.pd

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry