No abstract

With the integration of microgrids (MGs) in future distribution networks (DNs), it is essential to develop a practical model for the distribution company (DISCO). Optimal operation of MGs is not generally consistent with DISCO, especially when they are operated by private owners. To this end, a decentralized robust model for optimal operation of DISCO with private MGs (PMGs) is proposed in this paper. The objective is to minimize the total operation cost of the system including DN and PMGs through coordinated operation of them. The enforced operational uncertainties are handled using an adaptive robust optimization (ARO) approach, enabling the operators of DISCO and PMGs to adjust different conservation levels during operating horizon. To respect the ownership of PMGs, a decentralized algorithm based on alternating direction method of multipliers (ADMM) is proposed to efficiently solve the resulting ARO model in which the operating problems of DISCO and PMGs are optimized independently. Case studies of a test system including modified IEEE 33-bus distribution network with three PMGs is used to demonstrate the effectiveness of the proposed model.

Summary This paper investigates a stochastic bi‐level scheduling model for decision‐making of a load‐serving entity (LSE) in competitive day‐ahead (DA) and regulating markets with uncertainties. In this model, LSE as the main interacting player of the market sells electricity to end‐use customers and plug‐in electric vehicles (PEVs) to maximize its expected profit. Therefore, a two‐level decision‐making process with different objectives is considered to solve the problem. In one level, the objective is to maximize the LSE's profit by optimally scheduling of responsive loads and PEVs charging/discharging process, while in the other level, the payments of the customers and PEV owners should be minimized in a competitive market. In the proposed decision‐making process, to model the uncertainties, market prices, required energy of customers and PEVs, and the rival LSEs' prices are considered as random variables. The bi‐level stochastic problem is then converted into a linear single‐level stochastic model with equilibrium constraints by using Karush‐Kuhn‐Tucker (KKT) optimality conditions as well as duality theory. A case study is implemented to indicate the applicability of the intended model. The applicability of the proposed model is tested on Nordic market and the results show that in a competitive market, the LSE can increase its revenue and attract more demand loads and PEV owners by offering more moderate prices.

Summary Energy has been considered as one of the essential needs of mankind along with air, water, and food and witnessed evolution of civilization since evidence of human life. Managing energy resources is one of the challenging problems being capital intensive. Addressing this involves critical thinking and decision making with all possible aspects, technically known as set of primary and secondary criteria. There exist a number of literature sources addressing applications of multicriteria decision‐making (MCDM) in different energy‐related areas. Some are focusing on energy policy making, few are explaining site selection of solar PV, wind farm, and hydro power plants, and a few are describing applications in load management. Moreover, a few literature in this field elaborates various MCDM methods and their applications. In this article, an extensive and exhaustive study is carried out incorporating almost all possible applications of MCDM in renewable energy area. Various energy‐intensive applications are mapped with MCDM methods along with governing sensitive parameters. Hence, this study facilitates practicing engineers, decision‐makers, academician, and researchers to identify areas and MCDM techniques researched over the past decade in energy sector for planning, managing, selecting renewable resources, etc.

Summary The uncertainties related to renewable energy resources (RERs) and energy markets have a direct impact on the scheduling of hybrid microgrids and stand as a challenge in renewable‐based systems. Due to this, many mathematical methods are employed for modeling uncertainties, but applying a suitable approach is important for reaching accurate results. This paper presents the chance‐constrained programming technique to model the fluctuations in renewable outputs and electricity market prices to effectively deliberate the probabilistic nature of them. In this respect, the transactive energy concept is used to provide the energy sharing possibility for hybrid microgrids with a high portion of renewables for clean electricity generation. The interaction between the electrical and gas network as a result of using the gas‐fired devices in each microgrid, is also included in this study. To test the effectiveness of the suggested framework, the IEEE‐10 bus case study with five commercial hybrid microgrids is selected and the scheduling of microgrids is carried out in the interconnected electricity and gas networks. The different impacts of the proposed method are analyzed by considering two cases: optimal scheduling of hybrid microgrids without uncertainty modeling (Case I) and with it (Case II). In Case I and II, numerical results indicated $22 378.067 expected operation cost for Case II in comparison with $26 014.359 for Case I, which proves the effectiveness of the proposed model in probabilistic modeling of the system as well as achieving the economic benefits for hybrid microgrids. Highlights Optimal chance‐constrained DA scheduling of hybrid microgrids is effectively conducted. The CCP method is used for probabilistic evaluation of the problem in the presence of RERs. Transactive energy technology is employed for managing energy trading in the system. LHS and FFS methods are applied for scenario generation and reduction, respectively. The interactions between electricity and gas networks are effectively modeled.