A bi-level model for the design of dynamic electricity tariffs with demand-side flexibility

This paper addresses the electricity pricing problem with demand-side flexibility. The interaction between an aggregator and the prosumers within a coalition is modeled by a Stackelberg game and formulated as a mathematical bi-level program where the aggregator and the prosumer, respectively, play the role of upper and lower decision makers with conflicting goals. The aggregator establishes the pricing scheme by optimizing the supply strategy with the aim of maximizing the profit, prosumers react to the price signals by scheduling the flexible loads and managing the home energy system to minimize the electricity bill. The problem is solved by a heuristic approach which exploits the specific model structure. Some numerical experiments have been carried out on a real test case. The results provide the stakeholders with informative managerial insights underlining the prominent roles of aggregator and prosumers

Optimal management of demand response aggregators considering customers' preferences within distribution networks

In this paper, a privacy-based demand response (DR) trading scheme among end-users and DR aggregators (DRAs) is proposed within the retail market framework and by Distribution Platform Optimizer (DPO). This scheme aims to obtain the optimum DR volume to be exchanged while considering both DRAs’ and customers’ preferences. A bilevel programming model is formulated in a day-ahead market within retail markets. In the upper-level problem, the total operation cost of the distribution system, which consists of DRAs’ cost and other electricity trading costs, is minimized. The production volatility of renewable energy resources is also taken into account in this level through stochastic two-stage programming and MonteCarlo Simulation method. In the lower-level problem, the electricity bill for customers is minimized for customers. The income from DR selling is maximized based on DR prices through secure communication of household energy management systems (HEMS) and DRA. To solve this convex and continuous bilevel problem, it is converted to an equivalent single-level problem by adding primal and dual constraints of lower level as well as its strong duality condition to the upper-level problem. The results demonstrate the effectiveness of different DR prices and different number of DRAs on hourly DR volume, hourly DR cost and power exchange between the studied network and the upstream network.©2020 The Institution of Engineering and Technology. This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission and Distribution and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.fi=vertaisarvioitu|en=peerReviewed

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

Allocation of coal de-capacity quota among provinces in China: A bi-level multi-objective combinatorial optimization approach

© 2020 Elsevier B.V. Coal de-capacity, or capacity cut, is an important part of China's energy transition. Formulating a quota allocation scheme for coal de-capacity is the key to realizing smooth exit of coal overcapacity. This study proposes a novel method of allocation of coal de-capacity quota among provinces, based on bi-level multi-objective combinatorial optimization. In this bi-level optimal allocation scheme (BOAS), the upper level is the central government and the lower level is the provincial governments. The results indicate that, because of the different costs of coal de-capacity in each province, the execution rate of each province for tasks assigned by the central government is quite different. Compared with the government allocation scheme (GAS) and the single-level optimal allocation scheme (SOAS), the growth rate of total factor productivity of the BOAS increases by 2.14% and 0.60%, respectively; the total de-capacity cost of BOAS has reduced 64 billion yuan and 19 billion yuan, respectively; and the environmental benefits of BOAS has increased 73 billion yuan and 71 billion yuan, respectively; the Gini coefficient of BOAS calculated by various indexes is less than 0.3, placing the scheme within the category of considerable or absolute fairness. In addition, the proposed allocation model truly reflects the complex dynamics of the game process of China's coal overcapacity governance system, and can provide a more effective decision-making reference for the Chinese government in formulating the allocation scheme of coal de-capacity

Bilevel models for demand response in smart grids

RÉSUMÉ: La thèse porte sur les modèles d'optimisation mathématique biniveau et leurs applications à la réponse de la demande dans les réseaux électriques. L'augmentation de la production d'énergie renouvelable et l'apparition de nouveaux acteurs ont complexité les opérations et décisions dans les réseaux électriques. La nature aléatoire et distribuée de la génération solaire et éolienne entraîne un besoin d'ajustement de la production conventionnelle à la demande nette, correspondant à la demande après prise en compte de la production renouvelable. La réponse de la demande est une des solutions utilisées pour faire face à ces nouveaux besoins des réseaux électriques. Au lieu d'étudier l'adaptation de la production à la charge, son principe est d'exploiter la �exibilité d'une partie de la consommation, ajustant ainsi la courbe de demande au cours du temps. Dans la première partie de cette thèse, nous étudions un système de réponse de la demande par prix dynamique, TLOU. Dans ce système, un usager réserve une capacité pour une période donnée, et paie un prix dépendant du dépassement de sa capacité par la consommation sur la période donnée. Nous étudions les propriétés de ce système de tari�cation, en particulier du point de vue du fournisseur déterminant les paramètres de prix. L'interaction entre le fournisseur et les usagers est modélisée comme un jeu de Stackelberg ou meneur-suiveur qui est résolu par une approche d'optimisation mathématique biniveau. Les problèmes d'optimisation biniveau sont caractérisés par un problème d'optimisation imbriqué dans les contraintes d'un autre problème d'optimisation. Leurs champs d'applications incluent les problèmes de conception en ingénierie, les modèles économiques, les réseaux électriques ou encore la sûreté des systèmes. Dans la deuxième partie de la thèse, une formulation du problème biniveau est proposée dans laquelle le deuxième niveau n'est plus nécessairement résolu exactement, mais peut dévier de son optimum d'une quantité limitée. Nous développons une formulation biniveau robuste à la quasi-optimalité (NORBiP) dans laquelle le premier niveau s'assure de trouver une solution dont la faisabilité est garantie pour l'ensemble des solutions quasi optimales du deuxième niveau. Ce modèle introduit une notion de robustesse spéci�que à l'optimisation multiniveau. Une reformulation à un seul niveau est développée dans le cas où le deuxième niveau est un problème d'optimisation convexe, basée sur la dualisation des contraintes de robustesse. Dans le cas où le deuxième niveau est un problème linéaire, une formulation étendue linéarisée est proposée. Bien que cette formulation robuste soit plus di�cile à résoudre que le problème biniveau classique, nous établissons des résultats sur sa complexité, démontrant que le problème robuste à la quasi-optimalité appartient à la même classe de complexité que le problème optimiste équivalent sous certaines hypothèses. En�n, des algorithmes exacts et heuristiques sont proposés pour accélérer la résolution de problèmes biniveaux robustes à la quasi-optimalité dans le cas linéaire.----------ABSTRACT: This thesis investigates mathematical optimization models with a bilevel structure and their application to price-based Demand Response in smart grids. The increasing penetration of renewable power generation has put power systems under higher tension. The stochastic and distributed nature of wind and solar generation increases the need for adjustment of the conventional production to the net demand, which corresponds to the demand minus the renewable generation. Demand Response as a means to this adjustment of demand and supply is receiving growing attention. Instead of achieving the adjustment thanks to generation units, it consists in leveraging the �exibility of a part of the demand, thus changing the aggregated demand curve in time. The �rst part of this thesis focuses on a Time-and-Level-of-Use Demand Response system based on a price of energy that depends on the time of consumption, but also on a capacity that is self-determined by each user of the program. This capacity is booked by the user for a speci�c time frame, and determines a limit for energy consumption. Several key properties of the pricing system are studied, focusing on the perspective of the supplier setting the pricing components. The supplier anticipates the decision of the customers to the prices they set, the sequential decision created by this situation is modelled as a Stackelberg or Leader-Follower game formulated as a bilevel optimization problem. Bilevel optimization problems embed the optimality condition of other optimization problems in their constraints. Their range of applications includes optimization for engineering, economics, power systems, or security games. The inherent computational di�culty of bilevel problems has motivated the development of customized algorithms for their resolution. In the second part of the contributions, a variant of the bilevel optimization problem is developed, where the upper level protects its feasibility against deviations of the lower-level solution from optimality. More speci�cally, this near-optimal robust model maintains the upper-level feasibility for any lower-level solution that is feasible and almost optimal for the lower-level. This model introduces a robustness notion that is speci�c to multilevel optimization. We derive a single-level closed-form reformulation when the lower level is a convex optimization problem and an extended formulation when it is linear. The near-optimal robust bilevel problem is a generalization of the optimistic bilevel problem and is in general harder to solve. Nonetheless, we obtain complexity results for the near-optimal robust bilevel problem, showing it belongs to the same complexity class as the optimistic problem under mild assumptions. Finally, we design exact and heuristic solution methods that signi�cantly improve the solution time of the extended formulation

Modèles biniveaux pour la réponse de la demande dans les réseaux électriques intelligents

This thesis focuses on bilevel optimization, some variants, and an application to optimal price-setting in smart power grids.Bilevel optimization problems are a special subclass of constrained mathematical optimization problems where another problem, the lower level is embedded in the constraints.We consider their application to the optimal pricing of a Time-and-Level-of-Use Demand Response program, allowing an electricity supplier to leverage the flexibility of users through an economic incentive.A generalized form of bilevel optimization is also proposed where the lower level may pick a solution that is not optimal as typically assumed but near-optimal, that is feasible and within a fixed tolerance from an optimal solution.Solving this variant of bilevel optimization requires anticipation of the deviation from optimality and a guarantee that a solution remains feasible even with this deviation.Cette thèse étudie l'optimisation bi-niveau, certaines variantes et une application à la tarification dans les réseaux électriques intelligents.Les problèmes d'optimisation bi-niveaux sont une sous-catégorie de problèmes d'optimisation mathématique contrainte où un deuxième problème ou deuxième niveau estprésent dans les contraintes.Nous étudions leur application à un tariff variable en temps et en niveau de consommation, permettant à un fournisseur d'énergie d'exploiter la flexibilité de consommateurs par des incitations économiques.Une généralisation des problèmes bi-niveaux est également proposée, dans laquelle le deuxième niveau peut sélectionner une solution qui n'est pas optimale contrairement au modèle bi-niveau classique mais quasi-optimale.Résoudre cette variante de problèmes bi-niveaux demande l'anticipation de cette déviation de la solution de deuxième niveau de l'optimalité et garantit qu'une solution au problème bi-niveau sera réalisable malgré cette déviation

Advances in Energy System Optimization

The papers presented in this open access book address diverse challenges in decarbonizing energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids, and from theoretical considerations to data provision concerns and applied case studies. While most papers have a clear methodological focus, they address policy-relevant questions at the same time. The target audience therefore includes academics and experts in industry as well as policy makers, who are interested in state-of-the-art quantitative modelling of policy relevant problems in energy systems. The 2nd International Symposium on Energy System Optimization (ISESO 2018) was held at the Karlsruhe Institute of Technology (KIT) under the symposium theme “Bridging the Gap Between Mathematical Modelling and Policy Support” on October 10th and 11th 2018. ISESO 2018 was organized by the KIT, the Heidelberg Institute for Theoretical Studies (HITS), the Heidelberg University, the German Aerospace Center and the University of Stuttgart