Forbrukerfleksibilitet i kraftmarkeder

Demand flexibility integration is an important measure for the decarbonization of energy systems and a more efficient use of resources. Demand flexibility can provide multiple benefits to the power system and reduce system costs. Adjusting electricity demand to match variable production supports the integration of larger shares of variable renewable energy (VRE). Using demand response for system services provided by network operators can contribute to a more cost-efficient use of infrastructure and resources. Demand flexibility is a large and complex field of study which includes different markets, different grid voltage levels and different actors. The aim of this PhD project is to study how demand flexibility can be optimally integrated into electricity markets, taking account of the benefits to the power system as a whole and the interplay between different markets. Demand flexibility is studied from the perspective of the whole system, as well as from the private economic perspective of aggregators and electricity consumers. The thesis includes separate studies which go in depth about specific topics. The whole system perspective is studied in Paper I, which focuses on the value of demand flexibility in spot and reserve markets in power systems with high shares of VRE. The perspective of TSO and DSO is studied in Paper II, which proposes a marketplace for procurement of transmission and distribution system services from demand flexibility. The perspective of demand flexibility aggregator is studied in Paper III which develops an optimization framework for an aggregator participating in the wholesale and the regulation capacity markets. The perspective of private electricity consumers is studied in Paper IV which studies price-based demand response and investments in load control in an energy system. The results of these studies offer various useful insights. Firstly, demand flexibility was found to significantly decrease the system cost when large shares of VRE are integrated into the system. This happens primarily by replacing reserve provision from coal and gas plants but also by reducing peak load generation due to price response on the wholesale market. Optimal allocation of demand flexibility between reserve and wholesale markets maximizes the system benefits. The results suggest that in systems with large shares of VRE and small shares of base load, more demand flexibility should be placed in the reserve market than in the wholesale power market. Demand flexibility also benefits the distribution system, and it was also found that new market designs and better coordination between the transmission and distribution levels are important for efficiently integrating demand flexibility and minimizing the total procurement costs. New market designs can ensure that demand flexibility is used to maximize the value for the whole system and not only for single actors. Next, the results of the studies illustrate that demand flexibility access to many markets is beneficial, from both the system and private economic perspectives. It increases the value of demand flexibility, gives incentives to aggregators’ business and ensures that demand flexibility is optimally allocated between markets based on price. However, market interplay can also have negative effects, as when demand flexibility providers favour one particular market with higher profitability and flee from other markets. New market designs for demand flexibility should consider the interplay between different markets. Finally, modelling demand response to electricity price shows that private investments in demand flexibility are governed by the cost of load control, the daily electricity price variability and the price flattening effect. The price flattening effect implies that demand response to price reduces price volatility in the market, and at some point, no more demand response is feasible. To achieve this optimal demand response level in the wholesale market, it is important to have correct feedback between the market and consumers so that they do not respond more is optimal from the system perspective. To sum up, the results of this PhD research suggest that efficient integration of demand flexibility into electricity markets implies giving it access to many markets, strengthening the role of aggregators, improving coordination between the distribution and transmission system levels and promoting market designs that optimize demand flexibility use and system value. This thesis illustrates the importance of studying demand response in a holistic perspective, including different markets, actors and system levels.Norwegian Research Council ; Enfo ; Sysco ; NV

The competition and equilibrium in power markets under decarbonization and decentralization

Equilibrium analysis has been widely studied as an effective tool to model gaming interactions and predict market results. However, as competition modes are fundamentally changed by the decarbonization and decentralization of power systems, analysis techniques must evolve. This article comprehensively reviews recent developments in modelling methods, practical settings and solution techniques in equilibrium analysis. Firstly, we review equilibrium in the evolving wholesale power markets which feature new entrants, novel trading products and multi-stage clearing. Secondly, the competition modes in the emerging distribution market and distributed resource aggregation are reviewed, and we compare peer-to-peer clearing, cooperative games and Stackelberg games. Furthermore, we summarize the methods to treat various information acquisition degrees, risk preferences and rationalities of market participants. To deal with increasingly complex market settings, this review also covers refined analytical techniques and agent-based models used to compute the equilibrium. Finally, based on this review, this paper summarizes key issues in the gaming and equilibrium analysis in power markets under decarbonization and decentralization

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede

Optimal coordinated bidding of a profit maximizing, risk-averse EV aggregator in three-settlement markets under uncertainty

This paper develops a two-stage stochastic and dynamically updated multi-period mixed integer linear program (SD-MILP) for optimal coordinated bidding of an electric vehicle (EV) aggregator to maximize its profit from participating in competitive day-ahead, intra-day and real-time markets. The hourly conditional value at risk (T-CVaR) is applied to model the risk of trading in different markets. The objective of two-stage SD-MILP is modeled as a convex combination of the expected profit and the T-CVaR hourly risk measure. When day-ahead, intra-day and real-time market prices and fleet mobility are uncertain, the proposed two-stage SD-MILP model yields optimal EV charging/discharging plans for day-ahead, intra-day and real-time markets at per device level. The degradation costs of EV batteries are precisely modeled. To reflect the continuous clearing nature of the intra-day and real-time markets, rolling planning is applied, which allows re-forecasting and re-dispatching. The proposed two-stage SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs. Furthermore, the model statistics and computation time are recorded while simulating the developed algorithm with 5000 EVs