Explore the vast range of titles available.

Distributed generations (DGs) are main components for active distribution networks (ADNs). Owing to the large number of DGs integrated into distribution levels, it will be essential to schedule active and reactive power resources in ADNs. Generally, energy and reactive power scheduling problems are separately managed in ADNs. However, the separate scheduling cannot attain a global optimum scheme in the operation of ADNs. In this paper, a probabilistic simultaneous active/reactive scheduling framework is presented for ADNs. In order to handle the uncertainties of power generations of renewable-based DGs and upstream grid prices in an efficient framework, a stochastic programming technique is proposed. The stochastic programming can help distribution system operators (DSOs) to make operation decisions in front of existing uncertainties. The proposed coordinated model considers the minimization of the energy and reactive power costs of all distributed resources along with the upstream grid. Meanwhile, a new payment index as loss profit value for DG units is introduced and embedded in the model. Numerical results based on the 22-bus and IEEE 33-bus ADNs validate the effectiveness of the proposed method. The obtained results verify that through the proposed stochastic-based power management system, the DSO can effectively schedule all DGs along with its economic targets while considering severe uncertainties.

This paper introduces an optimal sizing approach for battery energy storage systems (BESS) that integrates frequency regulation via an advanced frequency droop model (AFDM). In addition, based on the AFDM, a new formulation for charging/discharging of the battery with the purpose of system frequency control is presented. The studied MG system that consists of PV units, a diesel generator (DG), a combined heat and power (CHP) unit, a gas boiler, and a BESS is designed to meet the consumers’ thermal and electrical load requirements as well as system frequency regulation. In the proposed optimization model, the net present value of expansion planning costs (EPC) over the project lifetime should be minimized according to the capacity of installed BESS. The EPC consist of four components including, (i) MG operation cost pertaining to the DG, CHP units and gas boilers, (ii) value of lost load, (iii) BESS investment cost, and (iv) replacement cost of BESS, recognizing its shorter lifetime relative to the project’s lifespan. The effectiveness of the proposed method and its advantages compared other methods are demonstrated via a case study simulation. Compared to the conventional frequency droop characteristic, the utilized AFDM can reduce the total EPC while a broader range of power/frequency control capabilities of the BESS is achieved to regulate the frequency in a desired band. Furthermore, the paper examines the impact of the AFDM on the selection of battery technology.

Integration of distributed generation (DG) units in distribution networks (DNs) has some benefits, such as improvement in voltage profile, decrease in power losses, and reduction in operation costs. In line with this concern, the achievement of these advantages, along with environmental benefits, can be further strengthened by the optimal placement and sizing of renewable‐based DGs. Reconfiguration is well known as another approach for optimizing the voltage profile and reducing energy losses in DNs. In this paper, a comprehensive model of simultaneous optimal reconfiguration and allocation of renewable energy DGs, including wind turbines (WTs) and biomass (BM) units in DNs, is presented, considering uncertainties of renewables and hourly load demands. In addition, environmental aspects of the proposed problem are taken into consideration by including emissions resulting from the use of other fossil fuel generations in the objective function. To cope with uncertainties in a robust framework, the information gap decision theory (IGDT) method is implemented. The proposed robust optimization model is examined on the IEEE‐33 node DN as a benchmark based on a discrete particle swarm optimization (DPSO) algorithm in MATLAB platform software. Various cases are considered to examine the impact of uncertainty budgets and robustness indices of different parameters on the results. The achieved simulation results are analyzed and compared with the other existing algorithms to verify the accuracy of the proposed method and its superiority over other algorithms in reducing costs and losses. In this paper, a comprehensive model of simultaneous optimal reconfiguration and allocation of renewable energy DGs including wind turbines (WTs) and biomass (BM) units in power distribution networks is presented considering uncertainties of renewables and hourly load demands.

In traditional power systems, besides the conventional power plants that provide the necessary reactive power in transmission system, the shunt capacitors along with the tap changers of transformers are also employed in distribution networks. In future years, because of the high number of distributed resources integrated into the distribution networks, it will be essential to schedule complete active-reactive power at distribution level. In this research work, an economic framework based on the active-reactive power bids has been developed for complete active-reactive power dispatch scheduling of smart distribution networks. The economical complete active-reactive power scheduling approach suggested in this study motivates distributed energy resources (DERs) to cooperate in both active power markets and the Volt/Var control scheme. To this end, using DER’s reactive power capability, a generic framework of reactive power offers for DERs is extracted. A 22-bus distribution test system is implemented to verify the impressiveness of the suggested active-reactive power scheduling approach.

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties.

One of the most important Distribution System Operators (DSO) schemes addresses the Volt/Var control (VVC) problem. Developing a cost-based reactive power dispatch model for distribution systems, in which the reactive powers are appropriately priced, can motivate Distributed Energy Resources (DERs) to participate actively in VVC. In this paper, new reactive power cost models for DERs, including synchronous machine-based DGs and wind turbines (WTs), are formulated based on their capability curves. To address VVC in the context of competitive electricity markets in distribution systems, first, in a day-ahead active power market, the initial active power dispatch of generation units is estimated considering environmental and economic aspects. Based on the results of the initial active power dispatch, the proposed VVC model is executed to optimally allocate reactive power support among all providers. Another novelty of this paper lies in the pricing scheme that rewards transformers and capacitors for tap and step changing, respectively, while incorporating the reactive power dispatch model. A Benders decomposition algorithm is employed as a solution method to solve the proposed reactive power dispatch, which is a mixed integer non-linear programming (MINLP) problem. Finally, a typical 22-bus distribution network is used to verify the efficiency of the proposed method.

One of the most significant control schemes in optimal operation of distribution networks is Volt/Var control (VVC). Owing to the radial structure of distribution systems and distribution lines with a small X/R ratio, the active power scheduling affects the VVC issue. A Distribution System Operator (DSO) procures its active and reactive power requirements from Distributed Generations (DGs) along with the wholesale electricity market. This paper proposes a new operational scheduling method based on a joint day-ahead active/reactive power market at the distribution level. To this end, based on the capability curve, a generic reactive power cost model for DGs is developed. The joint active/reactive power dispatch model presented in this paper motivates DGs to actively participate not only in the energy markets, but also in the VVC scheme through a competitive market. The proposed method which will be performed in an offline manner aims to optimally determine (i) the scheduled active and reactive power values of generation units; (ii) reactive power values of switched capacitor banks; and (iii) tap positions of transformers for the next day. The joint active/reactive power dispatch model for daily VVC is modeled in GAMS and solved with the DICOPT solver. Finally, the plausibility of the proposed scheduling framework is examined on a typical 22-bus distribution test network over a 24-h period.

Summary In this paper a new framework is introduced to develop a coupled active and reactive market in distribution networks. Distributed energy resources (DERs) such as synchronous machine–based distributed generations and wind turbines offer their active and reactive powers to the proposed market. For the considered DERs, multicomponent reactive power bidding structures are introduced based on their capability curves. Also, the hourly speed variations of wind turbines are considered in the proposed model. A distribution company buys active and reactive powers from a wholesale market and sells them via this market. The demand side is active, and responsive loads or aggregators can participate in the market using a demand bidding/buyback program. The objective function of the proposed market is to minimize the active and reactive power costs of DERs and distribution companies, the penalty cost of CO2 emission, and the cost of running a demand bidding/buyback program. The effectiveness of the proposed method is examined on a 22‐bus 20‐kV radial distribution test system.

This paper proposes a real‐time interactional pricing scheme to maximize the social welfare of players in real‐time demand response program in smart grids. Lagrangian relaxation–based dual decomposition is used to separate the social welfare optimization problem into a retailer's problem along with many consumers' subproblems, and the gradient projection method is adopted to solve them. First, the consumers' subproblems are solved to determine the optimal demand responses to the price announced by the retailer. To obtain the optimal demand response, a comprehensive mathematical function is developed on the basis of a combination of 5 costumer's utility functions reported in literature (ie, linear, potential, logarithmic, exponential, and hyperbolic). Afterward, the retailer calculates a real‐time price in response to the consumers' reactions to maximize its profit. In terms of practical implementation, the consumers and the retailer interact with each other via a limited number of control messages exchanges to find the optimal solution at each hour. The proposed method is evaluated considering the various retailer's cost functions and the consumers' behaviors. Also, the results of elasticity sensitivity analysis are presented from the retailer and consumer viewpoints.