A hybrid Jaya algorithm for reliability–redundancy allocation problems

© 2017 Informa UK Limited, trading as Taylor & Francis Group. This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results

A novel reliability oriented bi-objective unit commitment problem

© 2017 IEEE. This paper presents a new solution to unit commitment for single-objective and multi-objective frameworks. In the first step, the total expected energy not supplied (TEENS) is proposed as a separate reliability objective function and at the next step, the multi-objective Pareto front strategy is implemented to simultaneously optimize the cost and reliability objective functions. Additionally, an integer based codification of initial solutions is added to reduce the dimension of ON/OFF status variables and also to eliminate the negative influence of penalty factor. The modified invasive weed optimization (MIWO) algorithm is also developed to optimally solve the proposed problem. The obtained solutions are compared with results in the literature which confirms the applicability and superiority of the proposed algorithm for a 10-unit system and 24-hour scheduling horizon

Static Var Compensator allocation considering transient stability, voltage profile and losses

© 2017 IEEE. The purpose of this paper is to determine the optimal location, size and controller parameters of Static Var Compensator (SVC) to simultaneously improve static and dynamic objectives in a power system. Four goals are considered in this paper including transient stability, voltage profile, SVC investment cost and power loss reduction. Along with the SVC allocation for improving the system transient stability, an additional controller is used and adjusted to improve the SVC performance. Also, an estimated annual load profile including three load levels is utilized to accurately find the optimal location and capacity of SVC. By considering three load levels, the cost of power losses in the power system is decreased significantly. The combination of the active power loss cost and SVC investment cost is considered as a single objective to obtain an accurate and practical solution, while the improvement of transient stability and voltage profile of the system are considered as two separate objectives. The problem is therefore formulated as a multi-objective optimization problem, and Multi Objective Particle Swarm Optimization (MOPSO) algorithm is utilized to find the best solutions. The suggested technique is verified on a 10-generator 39-bus New England test system. The results of the nonlinear simulation indicate that the optimal sizing, location and controller parameters setting of SVC can improve significantly both static and dynamic performance of the system

A review on economic and technical operation of active distribution systems

© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods

A Linear-based Model for Multi-Microgrid Energy Sharing- A Western Australia Case Study

This paper proposes a model for energy sharing of interconnected microgrids (MGs), mainly where some MGs are owned by an entity, such as the government, which is the case study in Western Australia (WA). In the proposed model, MGs are able to trade energy among themselves when some of them have surplus generation, and others have lack of generations to meet their demand; however, they are obliged to pay for the use of distribution network, called network charge, and the share of network loss due to this energy transaction. In doing so, the network loss is taken into account and calculated through a power flow. The possibility of energy trading with the main grid is also considered through the wholesale electricity market. Considering the uncertainty of Photovoltaic (PV) generation and load involved, the decision making to inject or import energy to/from the main grid as well as to trade between MGs is obtained through a bi-level linear optimization. In the upper level, the distribution network operator intends to manage the energy exchange between MGs and energy trading with upstream grid, while in the lower level, each MG attempt to minimize its operational cost relating to PV and energy storage system (ESS). Finally, the proposed method is applied to a real project in Western Australia

An incentivized and optimized dynamic mechanism for demand response for managing voltage in distribution networks

The voltage regulation in distribution networks is one of the major obstacles when increasing the penetration of distributed generators (DGs) such as solar photovoltaics (PV), especially during cloud transients, causing potential stress on network voltage regulations. Residential demand response (DR) is one of the cost-effective solutions for voltage management in distribution networks. However, the main barriers of DR implementation are the complexities of controlling a large number and different types of residential loads, satisfying customers’ preferences and providing them fair incentives while identifying the optimum DR implementation locations and sizing as well as cooperating with the existing network equipment for the effective voltage management in the networks. A holistic and practical approach of DR implementation is missing in the literature. This study proposes a dynamic fair incentive mechanism using a multi-scheme load control algorithm for a large number of DR participants coordinating with the existing network equipment for managing voltage at medium voltage (MV) networks. The multi-scheme load control is comprised of short-interval (10-minute) and long-interval (2-hour) DR schemes. The dynamic incentive rates are optimized based on the energy contribution of DR participating consumers, their influence on the network voltage and total power loss improvement. The proposed method minimizes the DR implementation cost and size, fairly incentivizes the consumers participating in the DR and priorities their consumption preferences while reduces the network power losses and DGs’ reactive power contributions to effectively manage the voltage in the MV networks. An improved hybrid particle swarm optimization algorithm (IHPSO) is proposed for the load controller to provide fast convergence and robust optimization results. The proposed approach is comprehensively tested using the IEEE 33-bus and IEEE 69-bus networks with several scenarios considering a large number of DR participants coordinated with the DGs and on-load tap changer (OLTC) in the networks

Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage

© 2010-2012 IEEE. Penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in distribution systems is increasing, and it is crucial to investigate their impact on systems' operation scheme, reliability, and security. In this paper, expected energy not supplied (EENS) and voltage stability index (VSI) of distribution networks are investigated in dynamic balanced and unbalanced distribution network reconfiguration, including RESs and EES systems. Furthermore, due to the high investment cost of the EES systems, the number of charge and discharge is limited, and the state-of-health constraint is included in the underlying problem to prolong the lifetime of these facilities. The optimal charging/discharging scheme for EES systems and optimal distribution network topology are presented in order to optimize the operational costs, and reliability and security indices simultaneously. The proposed strategy is applied to a large-scale 119-bus distribution test network in order to show the economic justification of the proposed approach

An Effective Approach for Locational Marginal Price Calculation at Distribution Level

This paper develops an effective approach for the locational marginal price calculation for local generations in an active distribution network containing different types of distributed generators (DGs). The proposed approach is based on encouraging private units to reduce power loss and greenhouse gas (GHG) emissions. To this end, firstly, the distribution system operator (DSO) surplus profit, obtained by the reduction of power loss and GHG gas emission due to the operation of private units in the network, is considered as a financial source for encouraging private units. Then, according to the contribution of each private DG, the locational marginal price is calculated. The proposed approach is an effective and incentive-based approach for DSO to retain control over private units to reduce power loss and GHG emissions. The simulation results on a modified 118-bus standard distribution test system demonstrate the efficiency of the proposed approach compared to the previous approaches

Hybrid power plant bidding strategy including a commercial compressed air energy storage aggregator and a wind power producer

© 2017 IEEE. In this paper, a commercial compressed air energy storage (CAES) aggregator equipped with a simple cycle mode operation having the ability to work like a gas turbine is coordinated with a wind power aggregator (WPA) as a hybrid power plant to participate in electricity markets. In the proposed approach, the WPA uses the CAES to tackle its stochastic input and uncertainties related to different electricity market prices, and CAES can also use WPA to manage its charging/discharging and simple cycle modes more economically. A three-stage stochastic decision-making method is used to model the mentioned optimization problem which considers three electricity markets including day-ahead, intraday and balancing markets. The problem is formulated as a mixed integer linear programming which can be solved with available commercial solvers. Also, conditional value-at-risk is added to the problem to control the financial risk of the problem and offer different operation strategies for different financials risk levels. The proposed method can provide both bidding quantity and bidding curves to be submitted to the electricity markets which is tested on a realistic case study based on a wind farm and electricity market located in Spain. The results confirm that the proposed method can provide extra profit in joint operation, have more flexibility and reduce the financial risks