Integrated energy systems within communities play a pivotal role in addressing the diverse energy requirements of the system, emerging as a central focus in contemporary research. This paper contributes to exploring optimal scheduling in a smart community featuring multiple smart buildings equipped with a substantial share of distributed photovoltaic sources, shared energy storage, and controllable loads. The study formulates a two-stage scheduling optimization model incorporating multiple stakeholders, explicitly examining the game dynamics between the smart community operator and the customer load aggregator. The weights assigned to each optimization objective are determined using an analytic hierarchy process-entropy weight method to establish a balanced and nuanced approach. The ensuing optimal scheduling encompasses unit output and energy trading considerations, focusing on enhancing the system's economic viability, safety measures, and environmental sustainability. A comprehensive evaluation is conducted to validate the proposed model by comparing its performance with alternative operational strategies. The results show that the designed strategy can reduce the operating cost by 40.22% and increase the level of PV consumption by 22.57% compared to the traditional heat-based strategy, which is effective in mitigating power fluctuations, smartly responding to dispatch peak demand, enhancing new energy integration, and ensuring the security of grid operation. 

Optimized scheduling of smart community energy systems considering demand response and shared energy storage

Optimal design of solar/wind/energy storage system-powered RO desalination unit: Single and multi-objective optimization

Renewable Energy Communities (RECs) play an important role in driving the transition towards sustainable energy systems. In this context, energy storage systems are pivotal in mitigating fluctuations and uncertainties associated with variable renewable energy sources (VRES). Considering the variability of renewable energy generation and demand, it is imperative to conduct a comprehensive analysis under different conditions, investigating the roles of various energy storage systems in achieving optimal solutions. In this study, we propose an optimization framework for the optimal design and operation of energy systems combining both short-term and long-term energy storage technologies. The proposed framework integrates multi-objective optimization and multi-criteria evaluation to evaluate the potential of increasing the Self-Sufficient Ratio (SSR) of the system while minimizing the associated Levelized cost of exergy (LCOEx). Constraints are also defined in terms of maximal capacities of VRES production and energy storage. We provide Pareto frontiers allowing for the selection of compromises between SSR level and LCOEx. The systems include batteries, hydrogen production and storage, and thermal energy storage, achieving an SSR of 89%, around twice the SSR of a system with no energy storage. The results also reveal that hydrogen storage is required to reach SSR levels exceeding 60% and that its capacity increases with increasing VRES and storage availability. Lastly, this study delves into the synergistic interplay between local VRES and storage capacities in optimizing system performance, showing that both VRES and storage play significant roles in increasing the SSR of energy communities. 

Optimal combination of daily and seasonal energy storage using battery and hydrogen production to increase the self-sufficiency of local energy communities

The establishment of a multi-energy complementary heating system (MECHS) using abundant renewable energy is currently an effective way to solve the rapid growth of heating demands in rural areas. However, coupling schemes for different energy sources have become a critical barrier to the MECHS development. The entropy-TOPSIS method was used to conduct a comprehensive evaluation of MECHS, which provided a basis for optimizing system operation strategy. We built a test platform for MECHS based on solar-biomass-electricity energy in Tumxuk, Northwest China. Frist, six working modes were established according to the system operation control strategy. Second, we constructed the evaluation indexes of MECHS considering energy, environment and economy and determined their weights using entropy weight method. Third, the TOPSIS model was used to perform a comprehensive evaluation of the different working modes to identify optimal energy supply schemes. Our case study showed that the MECHS operated stably and met the indoor temperature demands of rural houses in cold regions. To maximize the heating efficiency of the system, it is necessary to reduce the types of energy appropriately. Next, based on entropy-TOPSIS model, the recommended order of energy supply mode for heating systems were solar-biomass energy (0.6202), solar-biomass-electric energy (0.6152), solar-electric energy (0.5895), biomass energy (0.4075), biomass-electric energy (0.3680), and electrical energy (0.3603). We found that the optimal energy supply mode was solar-biomass. Our results indicated that the entropy-TOPSIS model could provide practical recommendations for the design and operation strategies of MECHS in cold rural areas. 

Comprehensive evaluation of the working mode of multi-energy complementary heating systems in rural areas based on the entropy-TOPSIS model

The process of selecting an intelligent transportation system data collection scheme (ITSDCS) is a multi-attribute group decision-making (MAGDM) problem characterized by the involvement of several mutually supportive criteria. A major obstacle to the ITSDCS selection lies in dealing with uncertain information characterized by single-valued neutrosophic credibility numbers (SvNCNs), involving true fuzzy credibility numbers (FCNs), indeterminate FCNs, and falsity FCNs. Existing operators on SvNCNs suffer from insufficient ability to handle extreme values and lack of flexibility, and there is an urgent need to develop general and comprehensive operators to aggregate evaluation information. To address this gap, this study combines SvNCNs with the Dombi operator, the extended power average (EPA) operator, and the extended power geometric (EPG) operator. We propose the SvNCNs Dombi extended power weight average (SvNCNDEPWA) operator and the SvNCNs Dombi extended power weight geometric (SvNCNDEPWG) operator, along with their extended forms, and investigate some useful properties of these operators. The operator is effective in aggregating evaluation information in group decision-making scenarios, providing a higher degree of adaptability and flexibility in dealing with the complexity of the ITSDCS selection problem. The model also performs criteria weighting using the distance correlation-based CRiteria Importance Through Intercriteria Correlation (D-CRITIC) method and the principle of minimum identifying information. In addition, the proposed MAGDM model is used in a case study of ITSDCS selection to confirm its applicability and usefulness. The consistency, robustness, and effectiveness of the method were verified through sensitivity and comparative analysis. 

Multi-attribute group decision-making method using single-valued neutrosophic credibility numbers with the Dombi extended power aggregation operator and its application in intelligent transportation system data collection scheme selection

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance to improve the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving effectiveness and superiority of the proposed method. 

Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS

Many exsting interval-valued intuitionistic fuzzy multiattribute decision making methods in evaluating wireless sensor networks (WSNs) are baesd on real-valued measures, which may lose divergence information and affect the accuracy of decision making. In this paper, a novel similarity-divergence measure of interval-valued intuitionistic fuzzy values is proposed, which can overcome the drawbacks of real-valued measures, and the rationality of the proposed measure is further proved. The measure is applied to calculate fuzzy similarities of each alternative and a TOPSIS method is developed to obtain the preference order of the alternatives. Considering the different importance of each attribute, a nonlinear optimization model is constructed, and the state transition algorithm (STA) is used to determine the attribute weights. Finally, the proposed method is used to evaluate the designed WSNs. The experimental results verify the effectiveness and superiority of the proposed approach in solving multiattribute decision making problems.

Evaluation of Wireless Sensor Networks Based on State Transition Algorithm and Fuzzy TOPSIS

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Multi-objective optimization and decision making for integrated energy system using STA and fuzzy TOPSIS

Evaluation of Wireless Sensor Networks Based on State Transition Algorithm and Fuzzy TOPSIS