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• A workflow scheduling framework is proposed based on MAPE-K control model in Fog. • An ANFIS neural network is utilized to predict future resource load. • A dynamic resource management is applied to minimize the energy consumption. • An I-ALO-GWO algorithm is designed for workflow scheduling. Fog computing paradigm provides diverse processing resources and storage close to the edge of Internet of Things (IoT) networks. Workflow scheduling is an open issue in fog computing as it addresses the deployment of IoT workflow tasks on fog cells. Deploying workflow tasks on fog cells in an inefficient manner can have negative consequences. It can lead to bandwidth waste, resource depletion, and significant operating expenses. Additionally, the majority of scheduling evolutionary algorithms suffer from premature convergence and sticking in local optima. In this paper, a predictive energy-aware scheduling framework is proposed as a MAPE-K control model consisting of the four Monitor, Analyzer, Planner and Executer components with a shared Knowledge base in fog computing. First, a prediction method applying an Adaptive Network-based Fuzzy Inference System (ANFIS) into an analyzer component is introduced to predict future resource load. Second, a resource management strategy based on predicted resource load is presented to reduce energy consumption. Third, the Improved Ant Lion Optimizer (ALO) and weighted Grey Wolf Optimizer (GWO) are combined into a planner component called I-ALO-GWO for workflow scheduling. In the end, the decisions made in the previous steps are executed on the fog cells. The effectiveness of the I-ALO-GWO evolutionary algorithm has been tested on the IEEE CEC2019 benchmark functions. In addition, the proposed approach is evaluated practically under several famous scientific workflows using the iFogSim tool. Experimental results indicate that I-ALO-GWO improves the makespan up to 18 %, the energy consumption up to 17 %, the total execution cost up to 11 % and 26 % in terms of efficiency in comparison with the second-best results. [ABSTRACT FROM AUTHOR]

Conventional petrol vehicles emit a substantial quantity of greenhouse gases, leading to increasingly serious global warming problems. The expansion and development of renewable power generation technologies is conducive in promoting the use of electric vehicles, which are more environmentally friendly. This paper proposes a multi-objective power expansion model considering renewable energy policy objectives. The model regards the problem as a multi-period optimisation task, taking the newly installed capacity and the power generation capacity of each power generation technology as decision variables, and simulating the uncertain factors in the planning process using Geometric Brownian Motion and Monte Carlo approaches. The optimisation objective of the model is to minimise expected costs, reduce risk and environmental impacts, and incorporate changing policy objectives into the constraints to meet policy makers' expectations for renewable energy development. Then, a decentralised target search-based multi-objective evolutionary algorithm is proposed to solve the model. Its effectiveness is verified by a numerical example using real data from the Chinese power system. The experimental results show that the proposed algorithm exhibits improved performance compared with benchmark algorithms and provides high quality and diverse Pareto-optimal solutions to decision makers. Finally, the optimal plans for power expansion and generation mix under different preferences and policy objectives are discussed and corresponding recommendations are made. [Display omitted] • Planning for the expansion of power generation under uncertainties is determined. • Strategies for the expansion of power generation towards renewable energy are optimised. • A novel multi-objective evolutionary algorithm with a constraint handling method is developed. • The impact of multiple renewable energy policies on power generation expansion planning is discussed. [ABSTRACT FROM AUTHOR]

• Impact of demand response (DR) strategy on the optimal design of microgrid (MG). • Techno-economic feasibility assessment of a grid-tied MG considering DR strategy. • Proposing renewable Generation-based dynamic pricing to optimize MG configuration. • Employing the dandelion algorithm to solve the sizing problem of MG optimally. Integrating microgrids into the existing power system increases utility grid reliability and efficiency. Optimal sizing and configuration of Microgrid (MG) components are essential at the design stage for minimizing the overall cost and emissions of the MG. This paper presents a novel mathematical model for optimizing the configuration and operation of a grid-connected microgrid exploiting Demand-Side Management (DSM). This paper studies the impact of Demand Response (DR)-based DSM on optimal MG design from the customers' and utility's perspectives, considering both economic and technical performance metrics. A comparative performance study of six DR programs is investigated in order to verify the effectiveness of the proposed Renewable Generation-based Dynamic Pricing (RGDP)-based DR. In this paper, the sizing problem of MG is formulated as a bi-objective optimization in order to fulfil two objective functions: minimizing the total annual cost and minimizing life cycle emissions. An advanced evolutionary algorithm named the Dandelion Algorithm is utilized in order to solve the complex and constrained nonlinear optimization problem. The results demonstrate that considering RGDP-based DR in the MG sizing problem reduces the installation and operating costs of the MG without load shedding. The proposed DR strategy satisfies the customers' needs with high efficiency and low cost. [ABSTRACT FROM AUTHOR]

The self-generation and self-use attributes of distributed photovoltaic increase the line costs of grid enterprises, and these costs are mainly borne by regional power users and grid enterprises. With the expansion of distributed photovoltaic scales and the rise of profit rates, distributed photovoltaic operators should also become one of the main contributors to the grid connection costs. Distributed photovoltaic grid connection cost related researches focus on its impact on the regional users of transmission and distribution prices. Alternative apportionment of grid connection cost on distributed photovoltaic related subjects of the impact of the research is less. Based on the theory of evolutionary game, this paper constructs a four-party game model among roof owners, grid enterprises, PV enterprises and the government, and takes 30 regions in China as examples to measure the impacts of different grid connection cost sharing schemes on the decision-making of the stakeholders. At the same time, it considers the impacts of the DPV cost, the grid connection cost and the proportion of self-consumption of DPV on the results of the evolutionary game. The results of this study are used to propose policy recommendations on the sharing of grid connection costs for distributed photovoltaic in China. • The composition and channeling status of the cost of grid-connection of DPV in China was sorted out. • The economic and social benefits brought by DPV were comprehensively considered in the payment matrix. • DPV development under different cost sharing schemes of the cost of grid-connection of DPV were compared. • The effectiveness of different cost sharing schemes of the cost of grid-connection in different regions of China was measured. • The impact of cost changes on the effectiveness of sharing schemes was analyzed. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-level multi-energy management framework for the coordinated and interactive operation of heterogeneous multi-microgrids (MMGs) based on many-criteria optimality. With the proposed framework, the highly nonlinear and complex MMG multi-energy management (MMGMEM) problem is formulated into tri-level scheduling subproblems with multi-energy couplings and multi-level interactions, in which the multi-energy trading with energy networks and multi-energy couplings within MGs are optimized in the upper and middle level, and a middle level is added to correct scheduling decisions of the upper level for coordinating the MMG multi-energy sharing. Then, a multi-step matrix decomposition technique is developed to decompose the high dimensional multi-energy coupling matrix of MMGs into the sum of three linear and sparse submatrices for improving the computation efficiency and scalability. Furthermore, a many-criteria decision making (MCDM) model is proposed for the multi-energy sharing problem to achieve an optimum tradeoff in which all microgrids (MGs) can benefit from electricity-gas exchanges, and an evolutionary many-objective optimization based on hyperplane transformation algorithm is used to solve the MCDM problem. Simulation results verify that the proposed framework can achieve a cost saving for each MG (over 19%), and validate its scalability in solving large-scale MMGMEM problems. • A tri-level interactive framework is proposed for the coordinated operation of MMGs. • An extended multi-energy coupling matrix with MMG interactions is formulated. • A multi-step matrix decomposition technique is used to reduce the problem complexity. • A many-criteria optimality model is proposed for the tradeoff of all MGs' benefits. • The utilization of renewables can be enhanced with the minimal total operation cost. [ABSTRACT FROM AUTHOR]