Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

Power grids include entities such as home-microgrids (H-MGs), consumers, and retailers, each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs. Therefore, there is the need for a smart structure to handle the new situation. This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources (DERs) and energy storage in H-MGs by considering the demand response (DR). In general, the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs. The upper-level problem is decomposed into a set of low-level market clearing problems. Both electricity and heat markets are simultaneously modeled in this paper. DERs, including wind turbines (WTs), combined heat and power (CHP) systems, electric boilers (EBs), electric heat pumps (EHPs), and electric energy storage systems, participate in the electricity markets. In addition, CHP systems, gas boilers (GBs), EBs, EHPs, solar thermal panels, and thermal energy storage systems participate in the heat market. Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources, but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price

Fuzzy Bi-level Decision-Making Techniques: A Survey

© 2016 the authors. Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques

Special Section on Local and Distributed Electricity Markets

Driven by the Goals of Clean Energy and Zero Carbon Emissions, the Power Industry is Undergoing Significant Transformations. the Rapid Growth of Diverse Distributed Energy Resources (DERs) at Grid Edge Such as Rooftop Photovoltaics (PVs) and Electric Vehicles is Transforming the Traditional Centralized Power Grid Management to a Decentralized, Bottom-Up, and Localized Control Paradigm. Establishing Local and Distribution-Level Electricity Markets Provides an Effective Solution to Managing Large Amounts of Small-Scale DERs. New Regulations Such as the Recent FERC Order 2222 in the U.S. Open the Door to DERs in the Wholesale Markets. through Coordinating the Local and Distribution-Level Markets with the Transmission-Level Wholesale Market, the DERs and Prosumers Can Trade Energy and Flexibility Locally with Each Other and Meanwhile Provide Energy, Flexibility and Ancillary Services to the Bulk Power Grid. during This Transition, There Are Many New Technical Challenges to Address, Calling for Innovative Ideas and Interdisciplinary Research in This Promising Direction. Advanced Information and Communication Technologies (ICT) Are Needed, as a Key Enabler, for the Development and Practical Implementation of Local and Distribution Electricity Markets. Research into Local and Distribution Markets is Strongly Interdisciplinary, Involving the State of the Art in Power Engineering, Economics, and Digital/information Technology. a Broad Spectrum of Contributors from Universities, Industry, Research Laboratories and Policy Makers is Sought to Develop and Present Solutions and Technologies that Will Facilitate and Advance Practical Applications and Implementations of Local and Distribution-Level Electricity Markets to Uncover the Values of DERs

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners’ viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty―including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators―are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush–Kuhn–Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment

IoT-Enabled Operation of Multi Energy Hubs Considering Electric Vehicles and Demand Response

This paper introduces a novel Internet of Thing (IoT) enabled approach for optimizing the operation costs and enhancing the network reliability incorporating the uncertainty effects and energy management in multi-carrier Energy Hub (EH) and integrated energy systems (IES) with renewable resources, Combined Heat and Power (CHP) and Plug-In Hybrid Electric Vehicle (PHEV). In the proposed model, the optimization process of different carriers of Multi Energy Hubs (MEH) energy considers a price-based demand response (DR) program with MEH electrical and thermal demands. During the peak period, energy carrier prices are calculated at high tariffs, and other power hubs can help to reduce hub energy costs. The proposed model can handle the random behavior of renewable sources in a correlated environment and find optimal solution for turbines' communication in EHs. The simulation results show the high performance of the proposed model by considering the dependency between wind turbines in MEH structure, power exchange and heat among the EHs.© 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

A tri-level optimization model for inventory control with uncertain demand and lead time

We propose an inventory control model for an uncapacitated warehouse in a manufacturing facility under demand and lead time uncertainty. The objective is to make ordering decisions to minimize the total system cost. We introduce a two-stage tri-level optimization model with a rolling horizon to address the uncertain demand and lead time regardless of their underlying distributions. In addition, an exact algorithm is designed to solve the model. We compare this model in a case study with three decision-making strategies: optimistic, moderate, and pessimistic. Our computational results suggest that the performances of these models are either consistently inferior or highly sensitive to cost parameters (such as holding cost and shortage cost), whereas the new tri-level optimization model almost always results in the lowest total cost in all parameter settings

Network revenue management game in the rail freight industry

PhD ThesisThe study aims to design the optimal track access tariff to coordinate the relationship between an Infrastructure Manager (IM) and a Freight Operating Company (FOC) in a vertical separated railway system. In practice, the IM takes advantage of leader position in determining the prices to unilaterally maximise its profits without the collaboration with the FOC, which leads to a sub-optimal situation. The interaction between the IM and the FOC is modelled as a network-based Stackelberg game. First, a rigorous bilevel optimisation model is presented that determines the best prices for an IM to maximise its profits without any collaboration with the FOC. The lower level of the bilevel model contains binary integer variables representing the FOC’s choices on the itineraries, which is a challenging optimisation problem not resolved in the literature. The study proposes a uniquely designed solution method involving both gradient search and local search to successfully solve the problem. Secondly, an inverse programming model is developed to determine the IM’s prices to maximise the system profit and achieve global optimality. A Fenchel cutting plane based algorithm is developed to solve the inverse optimisation model. Thirdly, a government subsidy based pricing mechanism is designed. To identify the optimal amount of subsidy, a double-layer gradient search and local search method is developed. The proposed mechanism can lead to the global optimality and ensure that the IM and the FOC are better off than the above two scenarios. Numerical cases based on the data from the UK rail freight industry are conducted to validate the models and algorithms. The results reveal that both the optimal prices obtained via inverse optimisation and the subsidy contract outperform the non-cooperation case in the current industrial practice; and that the cooperation between the IM and the FOC in determining track access tariff is better than non-cooperation