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In this paper, a two-stage optimum planning and design method for a multi-carrier microgrid (MCMG) is presented in the targeted operation period considering energy purchasing and the component’s maintenance costs. An MCMG is most likely owned by a community or small group of public and private sectors comprising loads and distributed energy resources (DERs) with the ability of self-supply to regulate the flows of various energies to local consumers. The operation cost is undoubtedly reduced by selecting the proper components. In the proposed model, the investment and operation and maintenance costs of MCMG are simultaneously carried out in order to choose the right component and its size in the given period. Moreover, in this innovative model, net zero emission (NZE) is regarded as an environmental constraint. The genetic algorithm of MATLAB and the mixed-integer nonlinear programming (MINLP) technique of GAMS (general algebraic modeling system) software are used to solve the optimization problem. Illustrative examples show the efficiency of the proposed model.

A microgrid (MG) is a small-scale version of the power system which makes possible the integration of renewable resources as well as achieving maximum demand side management (DSM) utilization. The future power system will be faced with severe uncertainties owing to penetration of renewable resources. Consequently, the uncertainty assessment of system performance is essential. The conventional energy scheduling in an MG may not be suitable for active distribution networks. Hence, this study focuses on the probabilistic analysis of optimal power dispatch considering economic aspects in a multi-carrier networked microgrid. The aim is to study the impact of uncertain behavior of loads, renewable resources, and electricity market on the optimal management of a multi-carrier networked microgrid. Furthermore, a novel time-based demand side management is proposed in order to reshape the load curve, as well as preventing the excessive use of energy in peak hours. The optimization model is formulated as a mixed integer nonlinear program (MINLP) and is solved using MATLAB and GAMS software. Results show that the energy sharing capability between MCMGs and MCMGs and the main grids as well as utilization of demand side management can decrease operating costs for smart distribution grids.

Purpose Microgrids are inclined to use renewable energy resources within the availability limits. In conventional studies, energy interchange among microgrids was not considered because of one-directional power flows. Hence, this paper aims to study the optimal day-ahead energy scheduling of a centralized networked multi-carrier microgrid (NMCMG). The energy scheduling faces new challenges by inclusion of responsive loads, integration of renewable sources (wind and solar) and interaction of multi-carrier microgrids (MCMGs). Design/methodology/approach The optimization model is formulated as a mixed integer nonlinear programing and is solved using GAMS software. Numerical simulations are performed on a system with three MCMGs, including combined heat and power, photovoltaic arrays, wind turbines and energy storages to fulfill the required electrical and thermal load demands. In the proposed system, the MCMGs are in grid-connected mode to exchange power when required. Findings The proposed model is capable of minimizing the system costs by using a novel demand side management model and integrating the multiple-energy infrastructure, as well as handling the energy management of the network. Furthermore, the novel demand side management model gives more accurate optimal results. The operational performance and total cost of the NMCMG in simultaneous operation of multiple carriers has been effectively improved. Originality/value Introduction and modeling of the multiple energy demands within the MCMG. A novel time- and incentive-based demand side management, characterized by shifting techniques, is applied to reshape the load curve, as well as for preventing the excessive use of energy in peak hours. This paper analyzes the need to study how inclusion of multiple energy infrastructure integration and responsive load can impact the future distribution network costs.

Owners of electric vehicles (EVs) can offer the storage capacity of their batteries to the operator of a microgrid as a service called vehicle-to-grid (V2G) to hold the balance between supply and demand of electricity, particularly when the microgrid has intermittent renewable energy sources. Literature review implies that V2G has economic benefits for both microgrid operator and EV owners, but it is unclear how these benefits are divided between them. The challenge grows when the policy makers rely on the V2G revenue as an incentive for expanding the penetration of EVs in the automotive market. This paper models the interaction between microgrid operator and EV owners as a bargaining game to determine how the benefits of V2G should be divided. The method has been implemented on a hybrid power system with high wind penetration in addition to diesel generators in Manjil, Iran. The results indicate that, in addition to V2G benefits, government subsidies are necessary to promote the use of EVs.

This paper attempted to control a hybrid DC microgrid in islanded operation mode using decentralized power management strategies. Proposed adaptive I/V characteristic for hybrid photovoltaic (PV) and battery energy storage system (BESS) and wind turbine generator (WTG) adapts the distributed energy resources (DER) behavior independently in accordance with the load demand. Hence, the PV module can spend its maximum power on load demand and spend the extra power for charging the BESS, which will regulate DC bus voltage and maintain the power balance within the microgrid. When load demand is beyond the maximum generation power of PV unit, WTG will supply the energy shortage. The proposed control system was applied on the DC microgrid in order to achieve control objectives through a decentralized procedure, without telecommunication links. In order to validate the proposed strategies, the control system was implemented on a DC microgrid within MATLAB/SIMULINK, where the simulation results were analyzed and validated.

Renewable energies as a solution for environmental issues have always been a key research area due to Demand Response Programs (DRPs). However, the intermittent nature of such energies may cause economic and technological challenges for Independent System Operators (ISOs) besides DRPs, since the acceptable effective solution may exceed the requirement of further investigations. Although, previous studies emphasized employing Demand Response and Renewable Energies in power systems, each problem was investigated independently, and there have been few studies which have investigated these problems simultaneously. In these recent studies, authors neither analyzed these problems simultaneously nor discussed which scientific and practical aspects of demand response and renewable energy injection were employed. Motivated by this requirement, this research has focused on a comprehensive review of recent research of these cases to provide a comprehensive reference for future works.

The fault-induced delayed voltage recovery (FIDVR) and short-term voltage instability are increasing, especially due to the widespread implementation of residential air conditioners (RACs) in modern power systems. Single-phase induction motors in RACs have a high potential to stall in less than two to three cycles following a voltage dip in transmission or distribution systems. Using Shunt-FACTS devices, such as SVC and STATCOM, is a suitable solution for mitigating FIDVR events. In this paper, the Bayesian regularized artificial neural networks technique is employed to solve multidimensional mapping problems, taking into account the reactive powers injected into Busses. Following this, a multi-objective dynamic VAR programming is proposed to identify the optimal size of STATCOM for short-term voltage instability using trajectory sensitivities and heuristic optimization. This method is subject to complying with the criteria for dynamic and transient performance during FIDVR events. Dynamic VAR planning is carried out with assistance of the non-dominated sorting genetic algorithm II (NSGA-ӀӀ). The proposed multi-objective approach has been tested on the IEEE 39-bus system, taking into account time-varying practical load models. The results illustrate the effectiveness of the proposed approach in solving reactive power optimization problems while moderating the consequences of FIDVR.

Summary This paper presents a risk‐constrained programming approach to solve a retailer's medium‐term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope‐bound information‐gap model. Another source of uncertainty in this problem is the clients' demand, which is considered via a scenario generation method. The proposed method is formulated as a bi‐level stochastic programming problem based on the information‐gap decision theory. The Karush–Kuhn–Tucker optimality conditions are used to convert the bi‐level problem into a single‐level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Utilization of electric vehicle battery to provide frequency regulation service in electricity markets is a technically feasible and economically attractive idea. The role of aggregators as a middleman between electric vehicle owners and the frequency regulation market has been discussed in the literature. However, the economic interaction between the aggregator and the vehicle owners on division of interests is still a missing point. In this paper, a new pricing model for aggregators of electric vehicles is proposed such that not only its profit is maximized, but also the vehicle owners have sufficient incentives to take part in the offered vehicle-to-grid program. In the proposed model, the aggregator takes into account the depreciation cost of electric vehicle batteries and the cost of net energy transaction between the electric vehicles and the grid and, then, considers these items in settling accounts with vehicle owners. The proposed model has been implemented on PJM frequency regulation market, and the results are discussed in the paper.