This research article proposes a hybrid evolutionary framework based on hybridization of genetic algorithm (GA) and differential evolution (DE) for solving a nonlinear, high-dimensional, highly ...constrained, mixed-integer optimization problem called the unit commitment (UC) problem. Although GA is more capable of efficiently handling binary variables, the performance of DE is better in real parameter optimization. Thus, in the proposed hybrid framework, termed hGADE, the binary variables are evolved using GA while the continuous variables are evolved using DE. To test the efficiency of the presented framework, GA is hybridized with 4 classical and 2 state-of-the-art self-adaptive DE variants. We also incorporate a heuristic initial population generation method and a replacement scheme based on preserving infeasible solutions in the population to enhance the performance of the hGADE variants. A systematic classification of the proposed hybrid optimizer is presented in accordance with a recently proposed taxonomy in the literature. Extensive case studies are presented on different test systems and the effectiveness of the heuristic initialization, the replacement scheme, and the hybrid strategy is verified through stringent simulated results. We perform exhaustive benchmarking against some of the best algorithms proposed in the literature for UC problem to demonstrate the efficiency of the hGADE variants. Furthermore, the proposed hGADE variants are statistically compared among themselves to determine the best hGADE variants. Additionally, GA and DE are hybridized within multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework and the effectiveness of hybridization is demonstrated on multi-objective UC problem as well. The proposed hybrid framework is generic and other discrete and/or real parameter operators can be easily incorporated within the framework for solving different mixed-integer optimization problems.
In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) problem as a multiobjective optimization problem (MOP) considering ...minimizing cost and emission as the multiple objectives. Since UC problem is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables, while differential evolution (DE) evolves the continuous variables. Further, a novel nonuniform weight-vector distribution (NUWD) strategy is proposed and an ensemble algorithm based on combination of MOEA/D with uniform weight-vector distribution (UWD) and NUWD strategy is implemented to enhance the performance of the presented algorithm. Extensive case studies are presented on different test systems and the effectiveness of the hybrid strategy, the NUWD strategy, and the ensemble algorithm is verified through stringent simulated results. Further, exhaustive benchmarking against the algorithm proposed in the literature is presented to demonstrate the superiority of the proposed algorithm.
This paper proposes a distribution locational marginal pricing (DLMP) based bi-level Stackelberg game framework between the internet service company (ISC) and distribution system operator (DSO) in ...the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads (IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.
In real-time large-scale optimization problems, such as in smart grids, centralized algorithms may face difficulties in handling fast-varying system conditions, such as high variability of ...renewable-based distributed generators (DGs) and controllable loads (CLs). Further, centralized algorithms may encounter computation and communication bottlenecks while handling a large number of variables. To tackle these issues, consensus-based distributed strategies have been proposed recently. However, distributed computational intelligence (CI)-based techniques can provide a much better near-optimal solution within fewer iterations of the algorithm, which is a critical requirement in smart grids. Therefore, in this paper, a consensus-based dimension-distributed CI technique is proposed for real-time optimal control in smart distribution grids in which a large number of DGs and CLs are present. The proposed approach considers each DG or CL as a separate private entity, which is more relevant from the perspective of smart grid optimization. In the proposed consensus-based framework, each DG or CL is associated with an agent, and each agent is allowed to communicate only with its neighboring agents. The effectiveness of the proposed approach in terms of convergence, adaptability, and optimality with respect to a centralized algorithm and a benchmark algorithm is shown through simulations on 30-node and 119-node distribution test systems.
Decomposition is a well-known strategy in traditional multiobjective optimization. However, the decomposition strategy was not widely employed in evolutionary multiobjective optimization until Zhang ...and Li proposed multiobjective evolutionary algorithm based on decomposition (MOEA/D) in 2007. MOEA/D proposed by Zhang and Li decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them in a collaborative manner using an evolutionary algorithm (EA). Each subproblem is optimized by utilizing the information mainly from its several neighboring subproblems. Since the proposition of MOEA/D in 2007, decomposition-based MOEAs have attracted significant attention from the researchers. Investigations have been undertaken in several directions, including development of novel weight vector generation methods, use of new decomposition approaches, efficient allocation of computational resources, modifications in the reproduction operation, mating selection and replacement mechanism, hybridizing decomposition- and dominance-based approaches, etc. Furthermore, several attempts have been made at extending the decomposition-based framework to constrained multiobjective optimization, many-objective optimization, and incorporate the preference of decision makers. Additionally, there have been many attempts at application of decomposition-based MOEAs to solve complex real-world optimization problems. This paper presents a comprehensive survey of the decomposition-based MOEAs proposed in the last decade.
The main challenge in constrained multiobjective optimization problems (CMOPs) is to appropriately balance convergence, diversity and feasibility. Their imbalance can easily cause the failure of a ...constrained multiobjective evolutionary algorithm (CMOEA) in converging to the Pareto-optimal front with diverse feasible solutions. To address this challenge, we propose a dual-population-based evolutionary algorithm, named c-DPEA, for CMOPs. c-DPEA is a cooperative coevolutionary algorithm which maintains two collaborative and complementary populations, termed Population1 and Population2 . In c-DPEA, a novel self-adaptive penalty function, termed saPF , is designed to preserve competitive infeasible solutions in Population1 . On the other hand, infeasible solutions in Population2 are handled using a feasibility-oriented approach. To maintain an appropriate balance between convergence and diversity in c-DPEA, a new adaptive fitness function, named bCAD , is developed. Extensive experiments on three popular test suites comprehensively validate the design components of c-DPEA. Comparison against six state-of-the-art CMOEAs demonstrates that c-DPEA is significantly superior or comparable to the contender algorithms on most of the test problems.
This paper proposes a decentralized multiagent system (MAS) approach for service restoration using controlled distributed generator (DG) islanding. Furthermore, it investigates the impacts of ...vehicle-to-grid (V2G) facility of the electric vehicles (EVs) for service restoration. Service restoration is formulated as a multiobjective optimization problem considering maximization of priority load restored and minimization of switching operations as the multiple objectives and solved using the proposed decentralized MAS approach. Extensive case studies are conducted on 38, 69, and 119 bus distribution systems, and the following advantages of the proposed MAS approach are observed: 1) flexibility-to perform under different DG and EV penetration levels; 2) scalability-to restore service for different size test systems, small as well as large; and 3) robustness-ability to perform efficiently for both single as well as multiple-fault situations. The simulation results also highlight the benefits of V2G feature of EVs for service restoration.
This paper proposes a decentralized multiagent system (MAS) approach to solve the service restoration problem considering the uncertainty of load demand and renewable distributed generators (RDGs). ...Service restoration is formulated as a multi-objective optimization problem and solved by implementing controlled DG islanding using the proposed MAS approach. First, the uncertainty of load demand and RDG generation is forecast by generating scenarios using Monte Carlo simulations. Next, the expected nodes to be restored in an island are determined using a heuristic rule-based technique. Finally, the maximum likelihood of the expected island ranges to be restored are estimated using the maximum likelihood estimation method to assist the utility in decision making. Extensive case studies are conducted on 38 bus and 119 bus distribution system to show the efficacy of the proposed restoration strategy. The use of electric vehicles and battery energy storage as flexible sources of energy to alleviate the uncertainties in the system is also highlighted.
The increasing penetration of plug-in electric vehicles (EVs) to the electrical grid raises concerns over secure and economic operation of the system. A coordination mechanism between system operator ...and EV aggregators is necessary to ensure that the system is operated within the security limits, and to reduce the charging costs while satisfying EV users' energy needs. In this work, we present a cooperative hierarchical multi-agent system and propose an EV charging scheduling strategy in order to minimize the demand and energy charges while meeting the EV users' energy requirements and satisfying the system security constraints. Within the designed framework, the higher-level agents calculate a set of proposed control signals by solving the designated optimization problems, and send them to the lower-level agents to facilitate an optimal scheduling in line with the aforementioned objectives. Through this hierarchically distributed approach, it is possible to effectively coordinate multiple EV charging stations without the need of direct communication or any prior information related to EV arrivals. The computational complexity of the problem is reduced by distributing the work among agents, and the privacy of sensitive data, such as system topology, load profiles, and EV parameters, is preserved. Moreover, unlike the traditional distributed solution methods that converge iteratively, the proposed approach calculates the optimal charging schedule after a single round of communication. The efficacy of the proposed methodology is demonstrated by a series of case studies on 33-bus and 118-bus distribution test feeders.
In this paper, smart charging strategies incorporating a unified grid-to-vehicle and vehicle-to-grid charging framework are proposed for optimal integration of plug-in electric vehicles (PEVs) within ...the existing distribution system infrastructure. Two smart strategies with objective functions considering minimization of total daily cost and peak-to-average ratio, respectively, are developed to study the impact on PEV charging from an economic and technical perspective. The proposed strategies are implemented for PEV charging at workplace car parks located in a 37-bus distribution system and an analytical study is presented to evaluate the maximum possible PEV penetration that the existing distribution infrastructure can accommodate corresponding to the two strategies. A comparative analysis of the two strategies is performed in terms of various economic and technical benefits that are derived. Moreover, a performance comparison of the two strategies in presence of slow and fast charging of PEVs is also presented. Finally, an investigative study is conducted for both the strategies to evaluate the maximum PEV penetration that can be integrated in the upcoming years without infrastructure reinforcement. The simulation results present a comprehensive evaluation of the two proposed strategies.
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