Towards Structuring Smart Grid: Energy Scheduling, Parking Lot Allocation, and Charging Management

Nowadays, the conventional power systems are being restructured and changed into smart grids to improve their reliability and efficiency, which brings about better social, economic, and environmental benefits. To build a smart grid, energy scheduling, energy management, parking lot allocation, and charging management of plug-in electric vehicles (PEVs) are important subjects that must be considered. Accordingly, in this dissertation, three problems in structuring a smart grid are investigated. The first problem investigates energy scheduling of smart homes (SHs) to minimize daily energy consumption cost. The challenges of the problem include modeling the technical and economic constraints of the sources and dealing with the variability and uncertainties concerned with the power of the photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of power of PV panels, we propose a multi-time scale stochastic model predictive control (MPC). We use multi-time scale approach in the stochastic MPC to simultaneously have vast vision for the optimization time horizon and precise resolution for the problem variables. In addition, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool. Further, we propose cooperative distributed energy scheduling to enable SHs to share their energy resources in a distributed way. The simulation results demonstrate remarkable cost saving due to cooperation of SHs with one another and the effectiveness of multi-time scale MPC over single-time scale MPC. Compared to the previous studies, this work is the first study that proposes cooperative distributed energy scheduling for SHs and applies multi-time scale optimization. In the second problem, the price-based energy management of SHs for maximizing the daily profit of GENCO is investigated. The goal of GENCO is to design an optimal energy management scheme (optimal prices of electricity) that will maximize its daily profit based on the demand of active customers (SHs) that try to minimize their daily operation cost. In this study, a scenario-based stochastic approach is applied in the energy scheduling problem of each SH to address the variability and uncertainty issues of PV panels. Also, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool for the energy scheduling problem of a SH. Moreover, Lambda-Iteration Economic Dispatch and GA approaches are applied to solve the generation scheduling and unit commitment (UC) problems of the GENCO, respectively. The numerical study shows the potential benefit of energy management for both GENCO and SH. Moreover, it is proven that the GENCO needs to implement the optimal scheme of energy management; otherwise, it will not be effective. Compared to the previous studies, the presented study in this paper is the first study that considers the interaction between a GENCO and SHs through the price-controlled energy management to maximize the daily profit of the GENCO and minimize the operation cost of each SH. In the third problem, traffic and grid-based parking lots allocation and charging management of PEVs is investigated from a DISCO’s and a GENCO’s viewpoints. Herein, the DISCO allocates the parking lots to each electrical feeder to minimize the overall cost of planning problem over the planning time horizon (30 years) and the GENCO manages the charging time of PEVs to maximize its daily profit by deferring the most expensive and pollutant generation units. In both planning and operation problems, the driving patterns of the PEVs’ drivers and their reaction respect to the value of incentive (discount on charging fee) and the average daily distance from the parking lot are modeled. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing (SA) algorithm (QSA algorithm) and genetic algorithm (GA), respectively. We demonstrate that the behavioral model of drivers and their driving patterns can remarkably affect the outcomes of planning and operation problems. We show that optimal allocation of parking lots can minimize every DISCO’s planning cost and increase the GENCO’s daily profit. Compared to the previous works, the presented study in this paper is the first study that investigates the optimal parking lot placement problem (from every DISCO’s view point) and the problem of optimal charging management of PEVs (from a GENCO’s point of view) considering the characteristics of electrical distribution network, driving pattern of PEVs, and the behavior of drivers respect to value of introduced incentive and their daily distance from the suggested parking lots. In our future work, we will develop a more efficient smart grid. Specifically, we will investigate the effects of inaccessibility of SHs to the grid and disconnection of SHs in the first problem, model the reaction of other end users (in addition to SHs) based on the price elasticity of demand and their social welfare in the second problem, and propose methods for energy management of end users (in addition to charging management of PEVs) and model the load of end users in the third problem

Modeling a cooperation environment for flexibility enhancement in smart multi-energy industrial systems

Environmental aspects have been highlighted in architecting future energy systems where sustainable development plays a key role. Sustainable development in the energy sector has been defined as a potential solution for enhancing the energy system to meet the future energy requirements without interfering with the environment and energy provision. In this regard, studying the cross-impact of various energy vectors and releasing their inherent operational flexibility is main topic. Thecoordinationofvariousenergyvectorsundertheconceptofmulti-energysystem (MES)hasintroducednewsourcesofoperationalflexibilitytothesystemmanagers. MES considers both interactions among the energy carriers and the decision makers in an interdependent environment to increase the total efficiency of the system and reveal the hidden synergy among energy carriers. This thesis addresses a framework for modeling multi-energy players (MEP) that are coupled based on price signal in multi-energy system (MES) in a competitive environment. MEP is defined as an energy player who can consume or deliver more than one type of energy carriers. At first, the course of evolution for the energy system from today independent energy systems to a fully integrated MES is presented and the fractal structure is described for of MES architecture. Moreover, the operational behavior of plug-in electric vehicles’ parking lots and multi-energy demands’ external dependency are modeled in MES framework to enhance the operational flexibility of local energy systems (LES). In the fractal environment, there exist conflicts among MEPs’ decision making in a same layer and other layers. Realizing the inherent flexibility of MES is the main key for modeling the conflicts in this multi-layer structure. The conflict between two layers of players is modeled based on a bi-level approach. In this problem, the first level is the MEP level where the player maximizes its profit while satisfying LES energy exchange. The LES’s exchange energy price is the output of this level. In the lower level, the LESs schedule their energy balance, based on the upper level input price signal. The problem is transformed into a mathematical program with equilibrium constraint (MPEC) through duality theory. In the next step, high penetration of multi-energy players in the electricity market is modeled and their impacts on electricity market equilibrium are investigated. In such a model, MEP participates in the local energy and wholesale electricity markets simultaneously. MEP and the other players’ objectives in these two markets conflict with each other. Each of these conflicts is modeled based on bi-level programming. The bi-level problems are transformed into a single level mixed-integer linear problem by applying duality theory

Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio

Demand response for smart homes

RÉSUMÉ: Problèmes dans l’opération de la transmission d’électricité, surcharge, émission de carbone sont, entre autres, les préoccupations des gestionnaires de réseaux électriques partout dans le monde. Dans ce contexte, face au besoin de réduire les coûts d’exploitation ainsi que le besoin d’adaptation aux différentes exigences de qualité, de sécurité, de flexibilité et de durabilité, les réseaux intelligents sont considérés comme une révolution technologique dans le secteur de l’énergie électrique. Cette transformation sera nécessaire pour atteindre les objectifs environnementaux, intégrer la participation de la demande, appuyer l’adoption de véhicules électriques et hybrides ainsi que la production distribuée à basse tension. Chaque partie prenante dans le processus de gestion de l’énergie peut avoir des avantages avec le réseau intelligent, ce qui justifie son importance dans l’actualité. Dans ce travail, on se concentre plutôt sur l’utilisateur final. En plus de l’utilisateur final, nous utilisons également l’agrégateur, qui est une entité qui agrège un ensemble d’utilisateurs de sorte que l’union de leurs participations individuelles devienne plus représentative pour les décisions relatives au système d’énergie. La fonction de l’agrégateur est d’établir un engagement d’intérêts entre les utilisateurs finaux et l’entreprise de génération afin de satisfaire les deux parties. L’une des contributions principales de cette thèse est la mise au point d’une méthode qui donne à un agrégateur la possibilité de coordonner la consommation d’un ensemble d’utilisateurs, en maintenant le niveau de confort souhaité pour chacun d’entre eux et en les encourageant via des incitations monétaires à changer ses consommations, de sorte que la charge globale ait le coût minimal pour le producteur. Dans la première contribution (chapitre 4), ce travail se concentre sur le développement d’un modèle mathématique représentatif pour la planification des équipements d’un utilisateur. Le modèle intègre des modèles détaillés et fiables pour des équipements spécifiques tout en conservant une complexité telle que les solveurs commerciaux puissent résoudre le problème en quelques secondes. Notre modèle peut donner des résultats qui, comparés aux modèles les plus proches de la littérature, permettent des économies de coûts allant de 8% à 389% sur un horizon de 24 heures. Dans la deuxième contribution (chapitre 5), l’accent a été mis sur la création d’un cadre algorithimique destiné à aider un utilisateur final particulier dans son processus de décision lié à la récupération d’investissement sur l’acquisition d’appareils ou d’équipements (composants) intelligents. Pour un utilisateur spécifique, le cadre analyse différentes combinaisons de composants intelligents afin de déterminer lequel est le plus rentable et à quel moment il convient de l’installer. Ce cadre peut être utilisé pour encourager un utilisateur à adopter un concept de maison intelligente réduisant les risques liés à son investissement. La troisième contribution(chapitre 6) regroupe plusieurs maisons intelligentes. Un cadre algorithimique basé sur les programmes de réponse à la demande est proposé. Il utilise les résultats des deux contributions précédentes pour représenter plusieurs utilisateurs, et son objectif est de maximiser le bien-être social, en tenant compte de la réduction des coûts pour un producteur donné ainsi que de la satisfaction de chaque consommateur. Les résultats montrent que, du point de vue du producteur, la courbe de charge globale est aplatie sans que cela ait un impact négatif sur le confort des utilisateurs ou sur leurs coûts. Enfin, les expériences rapportées dans chaque contribution valident théoriquement l’efficacité des approches proposées.----------ABSTRACT: Transmission operation issues, overload, carbon emissions are, among others, the concerns of power system operators worldwide. In this context, faced with the need to reduce operating costs and the need to adapt to the different requirements of quality, security, flexibility and sustainability, smart grids are seen as a technological revolution in the field of power system. This transformation will be necessary to achieve environmental objectives, support the adoption of electric and hybrid vehicles, improve distributed low-voltage generation and integrate demand participation. Each stakeholder in the energy management process can have advantages with the smart grid, which justifies its current importance. The focus of this thesis is rather on the end user. In addition to the end-user, this work also uses the aggregator that is an entity that aggregates a set of users such that the union of the individual participation of each user becomes more representative for power system decisions. The function of the aggregator is to establish an engagement of interests between the end users and the generator company in order to satisfy both parties. One of the main contributions of this thesis is the development of a method that gives an aggregator the possibility to coordinate the consumption of a set of users, keeping the desired comfort level for each of them and encouraging them via monetary incentives to change their consumption such that their aggregated load has the minimal cost for the generator company. In the first contribution (Chapter 4), this work focuses on developing a representative mathematical model for user appliances scheduling. The model integrates detailed and reliable models for specific appliances while keeping a complexity such that commercial solvers are able to solve the problem in seconds. Our model can give results that, compared to the closest models in the literature, provide a cost savings in the range of 8% and 389% over a scheduling horizon of 24 hours. In the second contribution (Chapter 5), the focus was given in making a framework to help a specific end-user in their decision process related to the payback for an acquisition of smart appliances or equipment (components). For a specific user, the framework analyses various combinations of smart components to discover which one is the most profitable and when it should be installed. This framework can be used to encourage users towards a smart home concept decreasing the risks about their investment. The third contribution (Chapter 6) aggregates several smart homes. A framework based on demand response programs is proposed. It uses outputs from the two previous contributions to represent multiple users, and its goal is to maximize the social welfare, considering the reduction of costs for a given generator company as well the satisfaction of every user. Results show that, from the generator company perspective, the aggregate load consumption is flattened without impacting negatively the users’ comfort or their costs. Finally, the experiments reported in each contribution validate, in theory, the efficiency of the proposed approaches

Modelling and analysing the impact of local flexibility on the business cases of electricity retailers

Demand side response are proposed to incentivise customers to shift their electricity usage from peak demand periods to off-peak demand periods and to curtail their electricity usage during peak demand periods, which show great potential to reduce the peak loads, electricity prices, customers’ bills and further stabilize the power systems. The investigation of this effect on the pricing strategies and the profits of electricity retailers has recently emerged as a highly interesting research area. However, the state-of-the-art, bi-level optimization modelling approach makes the unrealistic assumption that retailers treat wholesale market prices as exogenous, fixed parameters. On the other hand, distributed energy resources (DER) in electricity markets are proposed to bring the significant operating flexibility which can support system balancing and reduce demand peaks, thereby limiting the balancing costs of conventional generators and the investments costs of new generation and network assets. And, local energy markets (LEM) have recently attracted great interest as they enable effective coordination of small-scale DER at the customer side, and avoidance of distribution network reinforcements. However, the introduction of LEM has also significant implications on the strategic interactions between the customers and incumbent electricity retailers, which has not been explored. Furthermore, a specific demand response technology of electric vehicles (EV) exhibits the potential to support system balancing and limit demand peaks, thus improving significantly the cost-effectiveness of low-carbon electricity systems. And the effective pricing of EV charging by aggregators constitutes a key problem towards the realization of the significant EV flexibility potential in deregulated electricity systems and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging / discharging levels. Furthermore, aggregators suffering from communication and privacy limitations are hard to acquire the perfect knowledge of EV operating characteristics and traveling patterns. Given such a context, this thesis aims at addressing the above challenges and proposing strategic retail pricing-based energy response programs to study the interactions between the electricity retailer / aggregator and its served flexible customers / EV based on game theoretic modeling and learning based approaches. We conduct the research in three different application scenarios: 1) This thesis proposes a novel bi-level optimization problem which represents endogenously the wholesale market clearing process as an additional lower-level problem, thus capturing the realistic implications of a retailer’s pricing strategies and the resulting demand response on the wholesale market prices. This bi-level optimization problem is solved through converting it to a single-level Mathematical Programs with Equilibrium Constraints (MPEC). The scope of the examined case studies is threefold. First of all, they demonstrate the interactions between the retailer, the flexible consumers and the wholesale market and analyse the fundamental effects of the consumers’ time-shifting flexibility on the retailer’s revenue from the consumers, its cost in the wholesale market, and its overall profit. Furthermore, they analyse how these effects of demand flexibility depend on the retailer’s relative size in the market and the strictness of the regulatory framework. Finally, they highlight the added value of the proposed bi-level model by comparing its outcomes against the state-of-the-art bi-level modelling approach. 2) This thesis explores for the first time the interaction between electricity retailer and LEM by proposing a novel bi-level optimization problem, which captures the pricing decisions of a strategic retailer in the upper-level problem and the response of both independent customers and the LEM (both including flexible consumers, micro- generators and energy storages) in the lower-level problems. Since the lower-level problem representing the LEM is non-convex, a new analytical approach is employed for solving the developed bi-level optimization problem. The examined case studies demonstrate that the introduction of an LEM reduces the customers’ energy dependency on the retailer and limits the retailer’s strategic potential of exploiting the customers through large differentials between buy and sell prices. As a result, the profit of the retailer is significantly reduced while the customers, primarily the LEM participants and to a lower extent non-participating customer, achieve significant economic benefits. 3) This thesis proposes a reinforcement learning (RL) method that the EV aggregator gradually learns how to improve its pricing strategies by utilizing experiences acquired from its repeated interactions with the EV and the wholesale market. Although RL can tackle the challenge of imperfect information and MPEC reformulation, the state-of-the- art RL methods require discretization of state and / or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This thesis proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy, and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements, and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners.Open Acces