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With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience. • A multi-objective integrated energy systems interaction model is established. • The interaction model considers multiple energy types and uncertainties. • The model can improve economy and flexibility of each system agent and the region. [ABSTRACT FROM AUTHOR]

A hybrid renewable energy system (HRES) utilizes the coaction of diverse energy to enhance energy efficiency while improving economic benefit. Under the paradigm of the electricity market, the HRES decomposes bilateral contract (BC) signed in the medium to long-term market and bids in the spot market are a promising way to get more revenue while ensuring basic profits by exploiting the synergy of different markets. However, the scheme of BC decomposition and the uncertainties of renewable energy affect the revenue of HRES scheduling. This paper proposes an optimal scheduling model for HRES to perform the BC flexible decomposition while bidding in the day-ahead energy market to maximize total revenue. The HRES is a real system located in Southwest China, and its constituent units are a pumped storage, two hydropower, and two solar stations. The Wasserstein metric ambiguity set is adopted to portray the uncertainty of available solar power and the forecasted market price error. Then, the proposed model is formulated as a distributionally robust chance-constrained scheduling model. Finally, the conditional value-at-risk approximation and strong duality are employed to reformulate this model into a tractable problem. The numerical results show that the proposed model can increase the total profit of HRES by 2.26 % (i.e., 2.56 × 104 DKK) compared to the case of fixed BC decomposition and bidding. Moreover, the impact analysis related to the ambiguity set and risk tolerance of uncertainty on total revenue of the proposed model is also presented, which provides the measure for the HRES to trade-off the robustness and economy of the solution. • The dispatch of the hybrid renewable energy system in the power market is studied. • A distributionally robust chance-constrained model is proposed accordingly. • The bilateral contract flexible decomposition is more profitable for the system. • The model can balance the economy and conservativeness of the solution. [ABSTRACT FROM AUTHOR]

In the last decade, the significant level of carbon emission in the atmosphere has increased the temperature of the Earth by 50% compared to the pre-industrial period. In recent years, considering renewable energy sources integrated with different energy systems such as cooling, heating, and power has become widespread around the globe as the ambition is to produce sustainable and clean energy. However, as the conventional methods for energy generation are highly reliable, their usage for supplying energy demands is inevitable. Therefore, in such renewable-based generation systems, the issues arising from uncertainties are the main challenges to the high penetration of these technologies. In this paper, the aim is to appraise such challenges in a multi-energy combined heat and power (CHP)-based microgrid integrated with power-to-gas (P-2-G) technology. In the proposed system, the downside risk constraint (DRC) approach as an effective risk management method is considered for risk assessment. The proposed model is a mixed integer linear program that is modeled as a multi-objective risk-based problem, for which the epsilon constraints method is used to achieve the optimal solutions for the objectives. According to the results, using P-2-G as an environmentally friendly tool can reduce approximately 55% of the emission cost. Further, the operating cost is increased nearly 13% to obtain a zero-risk condition for optimal management. [Display omitted] • Risk-constrained emission reduction and flexibility improvement in the fossil-fueled CHP-based multi-vector system. • A stochastic-based risk-aware multi-objective optimization framework with iterative solution mechanism. • Risk analysis with downside risk constraint (DRC) approach for the multi-vector uncertainties. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization strategy for hybrid-renewable energy systems in microgrids. The multi-objective optimization approach is formulated for a PV-wind-diesel-battery hybrid system. Its main objectives are to minimize the levelized cost of energy (LCOE) and loss of power supply probability (LPSP) whilst maximizing the use of renewable energy sources (RES). The proposed optimization strategy combines the Taguchi method with a novel fuzzy decision-maker-based multi-objective optimization algorithm. It implements, a) an energy management strategy to optimize the use of the energy sources, b) a Taguchi method to determine the upper bounds of the model's decision variables, c) a multi-objective moth flame optimization algorithm to optimize the size of the renewable energy sources, and d) a fuzzy decision-making approach to obtain the best Pareto front. The proposed strategy was implemented to optimize the design of a hybrid renewable energy system based on three different scenarios consisting of 10, 15, and 20 residential houses located in Sønderborg, a town in the Region of Southern Denmark. The results of the proposed model in terms of loss of power supply probability and levelized cost of energy for the scenarios I, II, and III are [0.224, 0.754], [0.313, 0.612], and [0.368, 0.547] respectively. In addition, the optimal design of the hybrid renewable-based microgrid system for scenarios I, II, and III are [PV: 32 kW, AD: 4.86, WT: 6], [PV: 36 kW, AD: 4.73, WT: 7], and [PV: 47 kW, AD: 5, WT: 7], respectively. The effectiveness of the proposed multi-objective optimization algorithm (MOMFO) in solving the optimization problem was examined and the results were further compared with those of the Non-Dominated Sorting Algorithm II(NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Social Engineering Optimizer (MOSEO). [ABSTRACT FROM AUTHOR]

Increased competition and reduced profit margins are pushing manufacturing plants to find ways to improve operational efficiency and reduce costs. At the same time, government incentives and community initiatives are encouraging the industrial sector to pursue sustainability in operation and energy consumption. This has introduced a paradigm shift where the focus of plant operation is moving from solely cost reduction to one that considers asset management, emission control, and utilization of alternative energy resources. Mathematically speaking, the problem is changing from a single objective optimization to one with multiple objective functions that may at times be contradictory. The focus of this paper is to propose a methodology for management of an industrial plant considering the above factors. It is assumed that the plant is equipped with distributed generation (DG), and can also participate in demand response (DR). Further, the effect of ambient temperature on the lifetime and available capacity of assets such as the plant transformer has been taken into account and incorporated into their daily schedule. The objective is then to find an optimal solution for operating various workstations in such a way that it minimizes the operational costs, the emissions produced, and the loss of life of assets exposed to excess temperatures. A multi-objective optimization problem is therefore formulated through extended lexicographic goal programming, which is further modified to ensure Pareto optimality. A case study has also been presented for a manufacturing plant for luxury vehicle cockpit assembly line. [ABSTRACT FROM AUTHOR]

As distributed renewable energy sources (RES) continue to expand, the necessity arises for more robust coordination approaches and conversion techniques to tackle the challenges introduced by uncertainties in renewable generation. Increasing concerns about energy efficiency and power grid security, such as the significant number of conversion stages for energy production from renewable resources and the bidirectional power flow in the distribution system, have attracted attention to the topic. The presented comprehensive review in this paper discusses the merits and weaknesses of different integration strategies as supported by the literature review. In particular, the focus is placed on the challenges of interconnection and opportunities for direct current (DC) systems. The ultimate objective of this paper is to explore the most important power grid-wide effects due to the expected renewable energy expansion and to gain insights on the availability characteristics of DC microgrids to facilitate their integration with the power grid. The idea behind this is to establish inferences that energy conversion based DC microgrids can be a possible solution to mitigate the negative effect of renewable energy expansion. This is accomplished by systematically reviewing studies on power grid integration as well as providing technical analysis of the resulting outcomes in relation to the general impact of renewable energy production, and then, conducting a comparative study on renewable energy conversion based DC and alternating current (AC) systems. The types of bus topologies and control of DC distribution systems are also intensively reviewed and discussed. Moreover, the protection design considerations, control classifications and standards of DC microgrids are highlighted to explore the future research trends to be undertaken. The review concluded that the impact of interconnection on power grid can be eliminated through the use of intelligent control with advance communication technologies and the implementation of DC microgrids powered by sustainable resources in the distribution system. • Energy production from renewable sources is reviewed by discussing the merits and weaknesses of grid integration. • Renewable energy conversion based DC system is the possible solution to overcome the issues of generation expansion. • The standards of DC microgrids need to be enhanced to ensure the security during different operating conditions. • The review confirms that efficient protective devices of DC systems have not been developed as compared to AC systems. [ABSTRACT FROM AUTHOR]

Generation of electricity comes with the emission of toxic gases into the atmosphere by the fossil fueled generators. Along with the promotion in the utilization of renewable energy sources (RES), it is also the duty of the power engineers to arrive at a compromised solution such that less emission of toxic gases occurs with economic generation of electricity. This paper proposes a balanced trade-off method for solving environment constrained economic dispatch (ECED) problems. A novel comparative analysis is performed among proposed ECED method with existing price-penalty-factor (PPF) and fractional programming (FP) methods for solving combined economic emission dispatch (CEED) problems on a 3-unit dynamic test system to sort out the method, which yields a better trade off solution between generation cost and pollutants emitted. An algorithm, following the hunting strategy of wolves, is improvised by incorporating strategies from population-based sine-cosine algorithm along with position updating methods of crows to form a robust hybrid algorithm, which was used as the optimization tool for the study. Involvement of RES diminished the generation cost to 5.5% for both economic dispatch and PPF based CEED, and 6.5% decrease in emission of pollutants was observed due to the same. The generation cost and amount of emitted pollutants, evaluated using proposed ECED approach, were much closer to the economic dispatch and emission dispatch values respectively compared to PPF based and FP based CEED solution. Furthermore, statistical analysis endorses the superiority of the proposed hybrid optimizer over other algorithms presented in the state-of-art literature. • Three different types of CEED methods are studied. • Subject test system is a 3-units RES integrated microgrid system. • Comparative analysis among the generation costs and pollutants emitted is performed. • A novel HMGWO algorithm was proposed as the optimization tool for the study. [ABSTRACT FROM AUTHOR]

Increasing energy efficiency in water distribution systems is one of the most important aspects of a sustainable water infrastructure. Additionally, providing this energy from renewable sources is an essential step towards a cleaner production in energy infrastructures. Therefore in this paper, an optimization model is developed to minimize the energy consumption of a water-energy microgrid system. A day-ahead economic dispatch model is developed to minimize the daily cost of energy in the water-energy microgrid. The energy consumption of water system is minimized using tank's and pump's scheduling and operation, hydraulic factors, and daily demand. Particularly, the electricity consumption of the pump is minimized by adjusting its head gain and flow rate via a variable speed. The energy unit is composed of an aggregated conventional power generation, solar photovoltaic, wind generation, and battery energy storage system. To offer a global optimum for the proposed non-linear programming formulation, bivariate piecewise linear approximation is used to linearize the pump's power consumption and gain, and conservation of energy-mass. In addition, univariate piecewise linear approximation is used to linearize the energy consumption function of conventional power generation systems. The optimization results of the mixed integer linear programming and mixed integer non-linear programming formulations for the studied water-energy microgrid system are compared and discussed. These models are developed to concurrently minimize the electricity consumption of a micro water-energy distribution network in two scenarios of (1) standalone water distribution system operation and (2) an integrated islanded micro water-energy system with electrical loads. • Energy consumption of standalone micro water system is formulated. • Co-optimization of energy and water demand in water-energy microgrid is studied. • Via piecewise linearization, nonlinear functions presented as linear approximates. • Unit commitment problem was formulated for distributed generation units. • A microgrid with battery storage and renewable sources is considered. [ABSTRACT FROM AUTHOR]

Over the last few decades, the use of renewable and clean energy sources has been the core of attention of the researchers. Besides, utilization of the energy hubs as a great innovation for future multi-carrier energy systems is well perceived for integrating intermittent sources of energy into the energy systems, which is surged and bolded in recent years. In this regard, this paper proposes an effective distributed energy management framework for modeling and optimal operation management of clean smart islands based on primal-dual method of multipliers. The primal-dual method of multipliers approach has shown superior performance compared to the alternating method of multipliers for distributed optimization. In this study, two different agents including a smart energy hub and a microgrid comprises of renewable and clean energy sources are considered in the smart island. These two distinct energy systems are assumed to be equipped with communication apparatuses and are intended to negotiate over the energy they need in a proper and completely distributed manner. Results show the effectiveness, accuracy and applicability of the approach for energy communication. • Providing a novel management scheme to properly supply the proposed system. • Incorporating a smart island, a smart microgrid and a SEH in remote areas. • Proposing a fully distributed energy management scheme based PDMM approach. • Investigating the distributed framework which covers demands in a smart island. [ABSTRACT FROM AUTHOR]