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Large-scale concentrated Variable Renewable Energy (VRE) sources pose a challenge to the balance and security of electricity systems. Studies have shown that the demand side may offer a greater responsiveness based on the shiftability of loads. However, there is currently no known literature on the demand response (DR) of large-scale shiftable loads in the residential sector. Firstly, there is no sufficiently large database of residential appliance-level load. Secondly, scheduling a large number of small residential shiftable loads simultaneously is a major challenge. Ultimately, the DR of large-scale residential buildings encounters the challenge of ensuring sufficient backup shiftable load to effectively cope with real-time VRE fluctuations during the response period. To address these issues, this paper proposes an appliance-level load data generation method based on diffusion neural networks. Meanwhile, a clustering method based on Generalized End to End (GE2E) loss neural networks is proposed to solve the complexity obstacles in simultaneous load swarm scheduling. Thirdly, a two-stage scheduling approach is proposed to address the challenges in the large-scale DR period through constructing day-ahead and real-time response models. The simulation results show that large-scale residential shiftable loads have a strong response capability to VRE fluctuations, and their response cost is lower than the conventional fuel-fired-power-plant-based-response within a certain user preference cost range. In practice, the clustering and scheduling methods proposed in this paper can be applied after the non-intrusive load monitoring (NILM) method is deployed to decompose the load data. • This paper addresses the research gap regarding large-scale residential load response to fluctuations in concentrated variable renewable energy sources. • A novel data generation model is proposed to address the issue of insufficient data scale in existing databases. • A novel load clustering algorithm is proposed to address the curse of dimensionality in simultaneous load swarm scheduling. • A novel two-stage response model is proposed is proposed to minimize the response cost and user preference loss. • When the unit user preference cost is lower than a certain boundary, the proposed response method is more cost-effective than conventional methods. [ABSTRACT FROM AUTHOR]

This paper explores the integration of microgrids with wind turbines to optimize electricity generation and enhance dispatch to distribution networks. The focus lies on a comprehensive examination of the microgrid configuration linked to a wind turbine, encompassing aspects such as the wind power generation system, variable-speed wind energy determination, fault estimation for stability analysis, and control through fault detection. Microgrid instability during network faults, attributed to limited capacity, is a primary challenge addressed in this study. The use of electronic interface power in islanded mode encounters hurdles due to the insufficient capacity of dispersed generation sources, leading to a slow response to load changes and difficulty in maintaining stability. To address these challenges, a novel microgrid topology and control method are introduced, featuring the integration of a battery to mitigate the slow response of micro-sources to load changes. Additionally, the concept of droop control is elucidated to effectively balance generation and consumption in islanded mode. The proposed approach employs fuzzy logic control systems along with a developed Kalman filter to precisely identify fault areas for stability analysis. Simulation results presented in this paper showcase substantial improvements in fault estimation, stability analysis, and various evaluation criteria. The abstract concludes by highlighting the keywords relevant to the study, emphasizing the significance of microgrids, energy storage systems, power converters, droop control, multiple control, and dynamic stability. In summary, this paper contributes to the discourse on renewable energy systems by presenting a comprehensive investigation into the integration of microgrids with wind turbines, offering valuable insights into improving stability, fault detection, and overall performance. [ABSTRACT FROM AUTHOR]

Abstract Optimal energy management of microgrids with different objectives such as operation cost, air pollution and wider use of renewable energy sources is investigated in many research works. In this paper, the effect of different operation modes of microgrids to solve environmental/economic dispatch problem is evaluated. Different modes of operation of microgrid include islanding operation, grid-connected and unilateral or bilateral exchange of power with main grid. The imperialist competitive algorithm (ICA) is one of the evolutionary algorithms that can model the competing imperialists to absorb colonies and uses it to find optimum solution. In this paper, a new method to improve the ICA algorithm is proposed in which the movement radius of the colonies toward the imperialist is adaptively adjusted in accordance with their position by means of adding direction and velocity parameters to the colonies. Thus, the colonies are attracted and repelled more intelligently by imperialists. The proposed method can create a trade-off between exploitation (i.e. local search) and exploration (i.e. global search) and prevents premature convergence while minimizing trapping in the local minima. Simulation results and comparison with other algorithms confirm high performance of the proposed algorithm. Graphical abstract Image Highlights • The environmental/economic dispatch problem for microgrids is evaluated. • Different modes of energy management operation of the microgrid have been studied. • A new method to improve the ICA algorithm is proposed. • The efficacy of the proposed improved ICA is extensively investigated. [ABSTRACT FROM AUTHOR]

Small islands are fragile and dependent territories in many sectors, especially energy. Hence, renewable energy microgrids (MGs) can offer an opportunity for environmentally sustainable energy supply but also a driver for new development strategies in these territories. This paper aims to analyse the role that MGs can play in insular spaces for the achievement of more sustainable communities. Initially, MGs played a crucial role in ensuring energy supply in spaces characterised by a difficult supply. However, with technological improvements in both generation and storage, as well as institutional and legislative changes, MGs can play a fundamental role in achieving more socially cohesive and environmentally sustainable spaces beyond their role as energy suppliers. We discuss three main topics after a meta-analysis of 18 MGs, both on-grid and off-grid, located in insular spaces: 1) the characteristics of MGs built in insular territories in the last 25 years; 2) MGs influence islanders' well-being and lifestyle; and 3) how MGs can encourage responsible consumption and promote postgrowth strategies. Thus, this paper explores the role that MGs have played in the past and present and what role they could play in the future of small island spaces. It might be concluded that MGs are a key factor in the development of more sustainable societies. However, they must be accompanied by changes in our economies, societies and lifestyles in a direction other than the pursuit of economic growth traditionally understood as a limitless increase in consumption and production. [ABSTRACT FROM AUTHOR]

This paper addresses the challenges inherent in Microgrids (MGs), Renewable Energy Sources (RES), and Plug-in Hybrid Electric Vehicles (PHEVs), presenting a pioneering approach for a sustainable energy future. The focal point is a novel Multi-Stage Dynamic Energy Management (EM) framework, strategically designed to optimize the use of non-dispatchable RES and PHEVs while reducing dependency on the Utility Grid (UG). The research integrates Solar Photovoltaic arrays (SPVs), Wind Turbines (WTs), Micro Turbines (MTs), Battery Energy Storage Systems (BESS), PHEVs, and loads, forming a comprehensive MG to enhance overall efficiency and resilience. The core innovation lies in the dynamic EM framework, minimizing UG dependency and adapting to stochastic RES generation and PHEV charging patterns. The study underscores the significance of demand response in enhancing energy efficiency and grid reliability. Demonstrating effective RES utilization, the research showcases the potential for a greener and more resilient energy infrastructure. Through advanced simulations, the proposed framework's performance is rigorously evaluated under various scenarios, including high RES penetration. The results not only affirm the framework's effectiveness but also highlight its potential to revolutionize MG management by reducing grid dependency and maximizing renewable resource utilization. [ABSTRACT FROM AUTHOR]

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]

The rise in distributed renewable generation and active prosumers has heightened power exchanges in Distribution Systems (DS), causing congestion issues. This paper presents a four-layer optimization model for Distribution System Operators (DSOs) to efficiently manage network congestion through smart prosumers. The first two layers involve an iterative bidding process in the energy market to maximize social welfare during settlement. The third and fourth layers address congestion in the market, employing an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm for decentralized coordination between DSOs and smart prosumers, including Microgrids (MGs), Energy Hubs (EHs), and Electric Vehicle Parking lots (EVPLs). Layer 1 optimizes market player bids, layer 2 maximizes social welfare, layer 3 handles congestion using various strategies, and layer 4 reschedules smart prosumers based on DSO's requests. Implemented on a 118-bus DS in GAMS, the model demonstrates the efficacy of the proposed adaptive ADMM in ensuring player privacy and market efficiency. Additionally, integrating the market-based mechanism with Distribution Feeder Reconfiguration (DFR) optimally utilizes smart prosumers, resulting in a 46.85% reduction in net DSO costs. This practical implication underscores the model's effectiveness in addressing network congestion challenges and providing economic benefits for DSOs. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are pivotal in the progression toward sustainable transportation. However, a growing body of recent studies has challenged the often-assumed notion that electric vehicles are often considered to be net-zero emissions. These studies suggest that electric vehicles could potentially generate substantial emissions due to their reliance on fossil fuels to generate electricity, especially when receiving fast charging services. This phenomenon has been mitigated with the use of renewable energy sources in the power system. However, due to the intermittent nature of renewable energy sources, additional requirements have arisen. To address this situation, this paper presents an interdisciplinary study of collaborative fast-charging stations (FCS) and distributed renewable energy planning based on the interactions between traffic and power networks in order to increase the adoption rate of electric vehicles and reduce emissions caused by traffic and power systems. The locations and sizing of fast-charging stations, distributed photovoltaic generation, and renewable energy power supply for electric vehicles are determined in the proposed strategy through multi-objective integer programming. The proposed planning strategy considers heterogeneous electric vehicle driving range constraints, the operation security of the power distribution system, the reliability and expansion of the current power distribution, as well as different types of air pollutant classifications resulting from the charging behaviors. To guarantee the feasibility and accuracy of the planning results, an interactive algorithm is applied to solve the multi-objective integer model. Several cases are conducted to validate the proposed approach. The results demonstrate that the proposed planning strategy has good performance in the planning of fast-charging stations and distributed renewable energy integration. • A joint planning approach is proposed. • Optimize the locations and sizing of the fast-charging stations and distributed renewable energy. • Quantify different types of air pollutant emissions during charging services to promote the adoption of EVs and renewable energy. • An integer multi-objective model with an interactive algorithm is proposed. • The results demonstrate that the proposed method confers significant advantages to the application of EVs and renewable energy. [ABSTRACT FROM AUTHOR]

In the context of climate change mitigation and sustainable energy infrastructure development, this paper focuses on the successful implementation of microgrids supplied by renewable energies in very diverse environments. Taking into account the challenges of reaching economies of scale, this paper is targeted at identifying success factors for microgrid implementation. The success factors are derived from case study analyses of microgrids implemented in the Global South and in communities of Germany. The goal of the research is to develop a mutual understanding of common values and so support future developments. The analysis covers the categories of ownership and participation; technology and system design; and policy and financing. The results show that microgrids in the Global South and renewable-energy-based communities in Germany share a number of success factors. The results demonstrate that a high share of ownership by users and the flexibility to expand the microgrid with user needs are particularly promising features. To verify the application of the identified success factors, the latter are applied to analyze the microgrid concept of “swarm electrification”. The analysis concludes that the concept of swarm electrification is consistent with the success factors, making it a high-potential approach for renewable-energy-based electrification. [ABSTRACT FROM AUTHOR]

The use of energy in industrial processes is a vital aspect of their proper functioning and productivity. However, with the increasing demand for energy and the need for more sustainable energy sources, managing energy efficiently has become crucial for industries. In this context, energy management systems (EMS) have emerged as a solution to integrate different energy sources and optimize their use. This work proposes a model for the optimal scheduling of a dairy industry based on an energy hub, considering the integration of renewable energy sources and Ice Storage. The proposed model aims to minimize daily operating costs by satisfying the electrical and thermal demands of the industrial process while considering the availability and costs of different energy sources. The model takes into account the use of microturbines (MT), photovoltaic (PV) sources, wind generation (WT), electrical storage systems (ESS), and energy from the electrical grid for the generation of electrical energy, and a boiler and an Ice Storage for thermal energy. A case study is conducted in this paper at the dairy farm belonging to UDLA in Nono, Ecuador, where the advantages of the proposed energy hub model for the dairy industry are analyzed. The suggested scheduling optimization model is solved using Pyomo library with a mixed integer linear programming method showing the benefits of including Ice Storage Systems and renewable energy. • Optimal scheduling of the dairy industry is presented based on energy hub. • Energy hub modeling enables sustainable dairy industry operations. • The use of ice storage is evaluated in dairy industry. [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]

Distributed energy resources (DER), encompassing distributed renewable resources and distributed energy storage systems, are progressively emancipating themselves from reliance on subsidies and engaging in the realms of energy, green certificate, and capacity markets. However, the long-term profile of DERs under multiple market conditions remains uncertain, and traditional methods are inadequate in addressing the challenges posed by the non-linearity and non-convexity of various factors that affect investment and bidding behavior. Additionally, the varying time scales of clearing models across different markets require a quantitative approach that combines long and short-term scales to determine DER profitability under various critical boundaries. To address these challenges, this paper proposes a dual-timescale dynamics model. Firstly, the system dynamics (SD) method is employed to establish the interplay among various market entities, simulating capacity investment of generation units on a long-term scale. Secondly, the stochastic unit commitment (SUC) is modeled to verify the operating constraints of the system while simultaneously calculating the short-term market clearing results. These results are then fed back to the long-term simulation loop to evaluate the further profitability of DERs. Lastly, a price adjustment mechanism was devised to simulate the frequent occurrence of negative energy prices in high-proportion renewable energy systems, thereby enhancing the fidelity of simulated price data to real-world environments. Through a 20-year simulation, the proposed model quantitatively analyzes the impact of market mechanisms on DER profitability and the influence of DERs on market clearing results. The model has the potential to provide valuable analytical support for the design of DER-related market mechanisms and policy decisions. • The impact of the energy, capacity, and tradable green certificate market on distributed energy resources is assessed. • The competitive relationship among thermal power, renewable energy, and energy storage in multiple markets are examined. • The long-term profile of distributed energy resources is evaluated by a dual-timescale dynamics model. • The impact of negative electricity prices on renewable energy sources (RES) is examined. [ABSTRACT FROM AUTHOR]

Abstract Eco-friendly technologies regarding electricity production are urgent need of sustainable energy development. The renewable energy resources (RERs) have brought green revolution in mitigation of greenhouse gaseous emission resulted from traditional energy resources (TERs). Moreover, the effective utilization of these resources is influenced by pricing schemes which have limitations. Therefore, this paper aims at optimization modeling for dynamic price-based demand response (DR) which includes flexible and inflexible loads along with the effective utilization of RERs i.e. photovoltaics (PVs) and wind turbines (WTs) in a microgrid (MG). The optimization problem regarding profit maximization for loads (flexible and inflexible) is solved via particle swarm optimization (PSO). Two cases are used to evaluate the performance of proposed dynamic pricing scheme. The simulation results have shown that proposed scheme is suitable in term of profit and comfort for flexible and inflexible loads as compared to fixed pricing scheme in both cases. In addition, the dynamic pricing scheme is exemplified as plug and play devices because of its easy implementation in present market structure without any modification. Highlights • The dynamic price-based demand response which includes flexible and inflexible loads. • The effective utilization of renewable energy resources in a microgrid. • The optimization problem is solved by particle swarm optimization. • Two cases are used to evaluate the performance of proposed dynamic pricing scheme. • This scheme is suitable regarding profit and comfort for flexible and inflexible loads. [ABSTRACT FROM AUTHOR]

Abstract Microgrids mainly use conventional and renewable energy resources at the same time. Conventional energy resources produce environmental pollution and need high cost for operation. In recent years, penetration of renewable resources such as photovoltaic and wind turbine has been rapidly grown in microgrids. Reduction of power losses and pollution are the main advantages of integrating renewable resources into networks. But, the renewable energy resources comprise low inertia and stability of the network integrated with such units is low. As a result, the environmental pollution and stability of microgrid are considered as the main problems and a new modeling of microgrid energy management is proposed by this paper to tackle such drawbacks. In this regard, environmental pollution is reduced by including hydrogen gas station and carbon capture-storage system. As well, the virtual synchronous generator is used to provide sufficient inertia and improving transient stability. The uncertain parameters are incorporated in the planning and stochastic programming is applied to tackle such uncertainties. Problem is mathematically expressed as a stochastic mixed integer linear programming and solved by the augmented Epsilon-constraint method. Finally, a comprehensive sensitivity analysis is carried out to evaluate the results. Based on the simulation results, by installing carbon capture-storage system, operational cost of microgrid is reduced from 64.998 $ to 56.043 $ and 1791.75 kg of carbon dioxide is stored. The revenue equal to 24 $ in one day is achieved by H 2 station without any pollution. The stability of microgrid is also significantly improved by installing virtual synchronous generator. The results demonstrate the viability and effectiveness of the proposed method to minimize environmental pollution, operation cost and frequency fluctuations in microgrid energy management. Graphical abstract Image Highlights • Stochastic microgrid scheduling is addressed incorporating different resources. • Considering carbon capture and storage system to reduce environmental pollution. • Considering H 2 station to reduce pollution and using extra renewable energy. • Solving microgrid transient stability by considering virtual synchronous generator. • Problem is solved by using the augmented Epsilon-constraint method in GAMS/CPLEX. [ABSTRACT FROM AUTHOR]

With a significant increase in concerns about global warming and lack of fossil fuels reserves as well as the requirements for clean energy production, the electrification of transportation in smart microgrids (SMG) has become an undeniable solution to respond to existing challenges. The distribution network operator (DNO), as responsible utility for optimal management of SMG, tries to optimize the procurement costs of network in such a way that all technical constraints of network are thoroughly met considering environmental obligations. In so doing, this paper proposes a novel eco-friendly scheme for optimal charging/discharging scheduling of plug-in electric vehicles (PEV) aggregators in SMG to minimize the procurement costs of system as well as reducing CO 2 emission with taking into account various types of PEV (i.e., BEV and PHEV). The Vehicle to Grid (V2G) capability as well as the actual patterns of drivers are taken into account in the proposed model. The impact of PEVs aggregation agents on the operation costs, purchasing power from upstream network, air pollution as well as technical specifications of system such as power losses and voltage profile has been investigated under practical constraints of PEVs, heterogeneous DERs and distribution network. In addition, the uncertainties subject to renewable generations are handled by stochastic programming to mitigate their minus effects on the profit of system. The weighted sum approach is employed to convert the multi-objective problem into a single-objective MINLP model and subsequently is minimized by collaborative grey wolf optimizer and Taguchi test method to acquire a satisfactory solution. Finally, an illustrative case study is provided to acknowledge the sufficiency of the proposed framework by performing it on the modified IEEE 69-bus system with integration of renewable resources. [ABSTRACT FROM AUTHOR]

The depletion of fossil fuels and growing environmental concerns have propelled the need for renewable energy sources. Micro-grids (MGs) have emerged as a viable solution for utilizing these resources effectively. This paper addresses the unit sizing problem for a two MG system using an intelligent method that considers air pollution emissions while catering to electrical and thermal loads. The MG incorporates photovoltaic (PV) panels, wind turbines (WTs), and a storage energy system comprising an electrolyzer, hydrogen tank, and fuel cell (FC) for combined heat and power (CHP) generation. In cases of excess power generation, an electric heater and backup boiler are employed to meet thermal loads. To account for air pollutant emissions, it is considered as a cost constraint in the objective function. Reliability is ensured by incorporating load curtailment costs as a limiting constraint. The MG is simulated in both islanding and grid-connected modes, with a generation planning program considering load growth. The IEEE RTS data is used for a peak 500 kW electrical load, and seasonal climate changes are factored in when deriving the thermal load. The modified particle swarm optimization (MPSO) algorithm is employed to find the global optimal solution. This research addresses key motivations, problems, and solutions in the field of renewable energy-based MGs. It introduces hydrogen tanks and batteries as storage systems to enhance system reliability. The integration of complementary energy sources, such as wind and solar, is proposed to improve system reliability and affordability. The use of batteries, solar, wind, and boilers for electrical and thermal load provision is more economically viable than hydrogen storage due to fuel prices. The proposed model significantly reduces pollution, enhances efficiency, decreases MG costs, and reduces dependence on fossil fuels. Integration with the grid reduces boiler usage, minimizes additional wasted power, generates income from power sales, and further reduces costs. Optimal size determination reveals the affordability challenges of wind and solar sources compared to microturbines, necessitating government intervention through subsidies or penalties for polluting resources. The results demonstrate that increasing subsidies expands the use of wind and solar resources, while the growth of fuel prices impacts resource size and necessitates storage use. Fine-tuning the penalty factor reduces pollution, but exceeding the limit reduces the adoption of renewables. Lastly, interest rate increases influence resource installations in different stages of the development plan. The outcomes of this study highlight the feasibility and effectiveness of the proposed model in addressing the challenges of MG design, operation, and integration of renewable energy sources. The findings contribute to the advancement of sustainable energy systems and provide valuable insights for policymakers and researchers in the field. [ABSTRACT FROM AUTHOR]

Policymakers and entrepreneurs are aware that reducing energy waste and underutilization are mandatory to actually foster the green transition. Nevertheless, small–medium enterprises usually meet technical and overwhelming financial constraints. They are unable to make profits, become less energy-sensitive, and cut down on their emissions simultaneously. Industrial districts are a source of both wealth and GHG (greenhouse gas) emissions. Eco-industrial parks (EIPs) supply a suitable strategy to ease symbiotic exchanges among various organizations. Surplus electricity from larger, energy-autonomous companies will be a new input for more vulnerable ones. This type of district is challenging, and it can provide an unexplored opportunity to cooperate, invest in renewable energy sources, and form alliances. To better exploit underutilized energy in industrial districts, it is essential to explore energy symbiosis (ES), i.e., an energy-based perspective of industrial symbiosis. This study presents an original mixed-integer linear programming (MILP) optimization model that aims to identify possible inter-firm exchanges and introduce microgrid-based support for distributed renewable-energy generators (DREGs) and battery energy storage systems (BESS) over a one-year simulation period. The model simultaneously targets economic and ecological objectives. The paper compares two case studies, one with battery support and one without. The optimization model was tested using a case study and found to improve energy efficiency (with a 43.46% saving in energy costs) and reduce greenhouse gas emissions (with an 84.59% reduction in GHG) by facilitating symbiotic exchanges among SMEs in industrial districts. The inclusion of BESS support further enhanced the model's ability to utilize green and recovered energy. These findings have implications for policymakers, entrepreneurs, and SMEs seeking to transition to more sustainable energy practices. Future work could explore the applicability of the MILP optimization model in other contexts and the potential for scaling up the model to larger industrial districts. • New MILP model for energy symbiosis park design developed. • Eco-industrial Park with shared renewable energy facilities and battery designed. • Model tested via two case studies: with batteries and without. • The analysis shows how batteries support enhance the park use of renewable sources. • Levelized Cost of Electricity of the park cut down of half the initial value. [ABSTRACT FROM AUTHOR]

To achieve the European climate targets and the Paris Agreements, at least 65% of the electricity needs to be generated from renewable energy sources by 2030. This requires a significant increase of distributed energy resources, posing a challenge for distribution system operators to integrate them into existing hierarchical grids. The concept of Local Flexibility Markets has recently gained attention as a market-based tool to tackle this challenge, making use of demand side flexibility. In this paper a Delphi method has been performed, showing that there are still numerous barriers in place preventing a widespread adoption of such markets in Europe. The main obstacles for market participants refer to standardisation issues. Based on that, a hybrid market model has been developed, comprising elements of a Local Flexibility Market and a Local Energy Market. To activate demand side flexibility from local energy transactions, spatio-temporally varying price signals are introduced, reflecting the constraints of the distribution grid. The paper shows, that this novel market approach helps to overcome relevant standardisation issues, but also certain barriers regarding end-users' lifestyles, which is because prices are comprehensible signals that can motivate end-users to participate. Moreover, a set of numerical examples is provided to illustrate the monetary benefits that could be gained by consumers and prosumers in the proposed hybrid market model. The examples show that the major share of the cost savings result from local energy trading, but the hybrid market model is also able to accumulate additional smaller revenues from providing flexibility. Finally, the systematic approach of characterising the market model in this paper offers a valuable framework for other researchers to map their ideas among existing approaches of Local Energy and Flexibility Markets. • A framework for characterising Local Energy and Flexibility Markets is provided. • Main barriers for adopting such markets are related to standardisation issues. • A hybrid market model is proposed, featuring local energy and flexibility trading. • Trades are steered by dynamic price signals, reflecting various flexibility needs. • Numerical examples indicate the expected financial implications. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic multi-layer energy management for a multi-microgrid (MMG) based smart distribution network (SDN). Also, all network constraints have been regarded to evaluate the effect of the active power loss on the energy management of the entities. Besides, demand response (DR) programs have been considered in the optimization process, which is one of the features of the SDN and could be used to manage the consumed power of each individual entity. Furthermore, in order to contemplate environmental issues, emission constraints for dispatchable generators have been taken into account. In this procedure, first, each microgrid (MG) performs an energy management to determine the schedule of its units and the shortage/surplus power. Then using the received data, the SDN operator (SDNO) prepares a priority list (PL) of units capable of injecting power to the SDN based on their average cost and capacity. The preparation of the PL entices the investors for using renewable based generators. Afterwards, it executes a global energy management. In the proposed approach, scenario based stochastic optimization has been utilized to model the uncertainties of the renewable energy resources. Finally, simulations using the modified IEEE 33-bus test feeder are done to demonstrate that how the proposed method encourages each MG for deploying clean productions as their main generation units. Moreover, the executed results illustrate the changes in the schedules of the MGs in the case of considering active power loss. [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]

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]

In the current close-to-smart power systems, renewable energies are the most significant elements that need to be carefully addressed in power system studies. In this regard, microgrids (MGs) have been introduced recently to activate the large penetration of renewables. However, integration of such generation technologies that are associated with severe uncertainty in their power output would significantly impact the scheduling of energy resources in MGs. Thus, efficient energy management systems are required to be employed in MGs. Therefore, this paper presents a day-ahead scheduling framework for an MG equipped with a solar photovoltaic (PV) unit. In this respect, different climate conditions and their impacts on the power output of the PV unit and the optimal scheduling of the MG have been investigated in this paper. To this end, four different days from the four seasons have been used to extract the data of solar irradiance. The scheduling problem has been formulated in a single-objective optimization framework, where the objective function is defined as minimizing the total operating cost over the scheduling period. An effective optimization algorithm named "modified krill herd (MKH)" algorithm is proposed to solve the mentioned day-ahead scheduling problem, while there are renewable and non-renewable generating units, besides an energy storage system. Furthermore, a comprehensive comparison has been made between the MKH algorithm and some well-known optimization algorithms to verify the superior performance of the suggested method. [ABSTRACT FROM AUTHOR]

With the widespread development of renewable energy sources, collective self-consumption (CSC) has been increasingly considered over recent years to sell the surplus of generated power to nearby consumers. However, appropriate energy allocation rules must be designed to efficiently share the energy among consumers. This paper aims to study two kinds of repartition keys for a CSC operation with multiple producers in the framework of an industrial multi-energy microgrid located in France. The default key considered in French regulations is compared to an optimized key based on the maximization of the income for the producers, in such a way as to maximize profitability by allocating the energy according to the consumers' electricity purchase price. The simulation results show that a CSC with an optimized key decreases the total electricity bill by 11.7%. For some producers, a reduction in the payback period between 6 months and 5 years is observed, compared to the case without CSC. A sensitivity analysis on economic assumptions is also proposed. The results show that the grid use tariff exemption is a key lever and makes it possible to find a more profitable selling price for producers and consumers, and to significantly reduce the payback period for investors. • A collective self-consumption (CSC) operation for an industrial microgrid is studied. • Two kinds of energy allocation rules are proposed and compared. • An optimization problem allocates the energy to increase the producers' income. • The CSC allows electricity bill savings and improves the profitability. • The results are sensitive to the CSC tariff and a grid use tariff exemption. [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]

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]

The U.S. industries accounted for 37% of the wind turbine capacity installed in 2020 and corporate solar purchases have totaled $22 billion over the last 10 years. However, affordability and intermittency of renewable generation are still the main obstacles for firms to power their operations using wind and solar energy. Aggregate production planning models that incorporate onsite renewable energy (APPMs-RE) are the state-of-the-art to facilitate renewable adoption. A large number of APPMs are available in the literature, but renewables-based APPMs are still inadequately investigated. This paper proposes a novel APPM-RE because it optimizes wide-ranging RE decisions such as microgrid capacity, energy storage, prosumer energy transactions, and demand response along with production, machine usage, and workforce levels to minimize cost. The model is a two-stage stochastic program considering uncertainties in product demand, labor and machine capacity, and power generation. The research goals are to (1) investigate the feasibility of decarbonizing the production, transportation, and warehousing operations, (2) examine the system affordability under practical hourly load requirements, (3) assess the cost-benefit of a prosumer energy transaction mechanism (PETM), (4) identify the effect in cost and microgrid capacity of time-of-use tariff, and (5) demonstrate the suitability of a two-stage stochastic programming model. The numerical experiments relying on climate analytics of six U.S. cities represent a broad scope of climate conditions. The managerial insights derived from this study are: (1) renewable microgrids with PETM may attain net-zero carbon operations with an affordable levelized cost of energy, and (2) time-of-use stimulates more adoption of solar generation than the flat rate. The Aggregate Production Planning Problem with Renewable and Prosumer Microgrids in a Multi-Site Manufacturing Supply Chain System. [Display omitted] • Jointly optimizes aggregate production planning and microgrid sizing decisions. • Considers uncertainties in products' demand, labor capacity, machine capacity and microgrids generation. • Incorporates time-of-use, prosumer energy transactions, and variable hourly load. • Includes probability distributions for hourly capacity factors of the renewables. • A holistic approach to assess the viability of onsite renewables in supply chains. [ABSTRACT FROM AUTHOR]

In the context of transition towards cleaner and sustainable energy production, microgrids have become an effective way for tackling environmental pollution and energy crisis issues. With the increasing penetration of renewables, how to coordinate demand response and renewable generations is a critical and challenging issue in the field of microgrid scheduling. To this end, a bi-level scheduling model is put forward for isolated microgrids with consideration of multi-stakeholders in this paper, where the lower- and upper-level models respectively aim to the minimization of user cost and microgrid operational cost under real-time electricity pricing environments. In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper-levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels. After that, the real-time prices updated by the upper-level model and the electricity plans determined by the lower-level model will be alternately iterated between the upper- and lower-levels through the real-time pricing mechanism to obtain an optimal scheduling plan. The test results show that the proposed method can coordinate the uncertainty of renewable generations with demand response strategies, thereby achieving a balance between the interests of microgrid and users; and that by leveraging demand response, the flexibility of the load side can be fully exploited to achieve peak load shaving while maintaining the balance of supply and demand. In addition, the Jaya-IPM algorithm is proven to be superior to the traditional hybrid intelligent algorithm (HIA) and the CPLEX solver in terms of optimization results and calculation efficiency. Compared with the HIA and CPLEX, the proposed method improves the MG net revenue by 10.9% and 11.9%, and reduces the user cost by 6.1% and 7.7%; our approach decreases the calculation time by about 90% and 60%. • A bi-level stochastic programming-based microgrid scheduling model is formulated. • A real-time pricing mechanism is designed to regulate supply-demand relationships. • A hybrid analytic-heuristic solution algorithm, called Jaya-IPM, is developed. • Demand response and uncertain renewable generations can be coordinated. • Validity and superiority of the proposal are verified by a microgrid test system. [ABSTRACT FROM AUTHOR]

Microgrids are an attractive way to deal with energy depletion and environmental deterioration as well as grid pressure. However, the energy configurations of microgrids comprising various uncertain and variable resources are significantly sophisticated. Focusing on the peak shaving ability and carbon reduction effect of microgrids, this paper proposes a multi-criteria evaluation method that aims to maximize the comprehensive value of microgrids. To unify these criteria with different perspectives when solving the optimization problem, the peak shaving and emission reduction effects are converted into economies through peak load cost and carbon tax rather than using the weighting method. The results of a case study in Higashida, Japan obviously evidenced the superiority of multi-objective optimization. Although the improvement of the peak shaving ability and emission reduction effect was partially at the expense of system cost benefit, the total cost under multi-criteria was still reduced by 2.13%, which indicates that the balance and optimization of the economic benefit, peak shaving and carbon reduction were realized. The effect of each evaluation criterion on the optimal configuration was analyzed by comparing the optimization results under objective functions with different criteria combinations. High requirements for carbon reduction can promote the development of renewable energy, and peak shaving ability should be ensured by controllable and stable power generators. • Analyze the characteristics of grid load for five multi-types buildings in Japan. • Propose evaluation criteria including, economic, peak shaving and CO 2 emission. • Introducing peak load price and CO 2 tax, evaluation criteria are converted into cost. • The configuration of microgrid is optimized quantitatively under multi-evaluation. • Effect of each criterion is carried out by comparing different criteria combinations. [ABSTRACT FROM AUTHOR]

The development of novel grid-connected integrated renewable energy systems for sustainable residential buildings is vital. The aim of this paper is to analyze the energy, economic and environmental performance of a novel grid-connected renewable energy system consisting of solar hybrid PV-Thermal collectors, wind turbine and sensible heat storage. The new integrated system serves concurrently hot water and electricity loads to a single-family building located in Laayoune, Morocco. The system adopts a stratified thermal storage and an auxiliary back-up to satisfy continuously the thermal load. Energy interaction between the examined system, load and grid is predicted at regular time intervals of 0.25 h. For a wind capacity of 1.2 kW, PV-T thermal collectors with a total area of 2.84 m2 and a storage tank of 0.3 m3, the system was able to satisfy 43.08% and 64.8% of the electric and heat demand, respectively. Compared to three other power supply scenarios, the economic assessment of the proposed configuration revealed that its Net Present Cost (NPC) is lower by 20.7%, 10.7% and 11.5% compared to grid only, PV-T only and wind only scenarios, respectively. As such, the novel system presents promising development perspectives especially that it allows additionally a reduction of up to 54% in the annual CO 2 emissions compared to the conventional energy supply mode relying entirely on the electric grid. • A novel hybrid PV-T/Wind system with thermal storage for electricity and heat generation • Self-consumption and self-sufficiency ratios are 90.25% and 43.08%, respectively. • Net present Cost of the proposed system is significantly lower than other options • The system can achieve an annual CO2 emission reduction of 54% [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]

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]

The penetration level of intermittent power generation into power systems has been substantial during recent years, which in turn highlights the need for installing storage systems. Renewable energy sources have been widely integrated into distribution systems and microgrids. One effective solution would be utilizing battery energy storage systems, which can provide the system with various merits like ancillary services and enhanced power quality, mainly due to their high power density and fast response. Accordingly, the problem of resource scheduling of microgrids with volatile power generation and storage systems needs to be further studied, and an effective model should be presented. In this respect, this paper investigates the problem of day-ahead operation of a grid-connected MG, integrated with distributed generation units and storage systems. The problem has been modeled an optimization problem while the objective has been assigned to the model as the total cost minimization, subject to different constraints, both system constraints and assets' constraints. Such constraints further complicate the original problem and an efficient solution method is required to tackle the problem as a large-scale optimization one. Thus, θ-modified krill herd approach is employed to solve the problem and provide the decision maker with an efficient solution. The simulation has also been conducted using a test MG and the results, obtained have been validated by comparing the results obtained from the presented method and those ones, derived from some well-known optimization algorithms. [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]

Formulating a novel mixed integer linear programing problem, this paper introduces an optimal Internet-of-Things-based Energy Management (EM) framework for general distribution networks in Smart Cities (SCs), in the presence of shiftable loads. The system's decisions are optimally shared between its two main designed layers; a "core cloud" and the "edge clouds". The EM of a Microgrid (MG), covered by an edge cloud, is directly done by its operator and the Distribution System Operator (DSO) is responsible for optimising the EM of the core cloud. Changing the load consumption pattern, based on market energy prices, for the edge clouds and their peak load hours, the framework results in decreasing the total operation cost of the edge clouds. Using the optimal trading power of the MGs aggregators as the input parameters of the core cloud optimisation problem, the DSO optimises the network's total operation cost addressing the optimal scheduling of the energy storages. The energy storages are charged in low energy prices through the purchasing power from the market and discharged in high energy prices to meet the demand of the network and to satisfy the energy required by the edge clouds. As a result, the shiftable loads and the energy storages are used by the DSO and the MGs to meet the energy balance with the minimum cost. • IoT-based optimisation of smart city energy management under a multi-stage problem. • Formulating a novel IoT-oriented MILP problem to smart city energy management. • Decision making is shared between layers as edge clouds (MGs) and a core cloud (DSO). • Obtaining mathematically optimal energy balance of the core and edge clouds. • Analyzing the effect of the shiftable load consideration in a near real-world application. [ABSTRACT FROM AUTHOR]

The generation and integration of renewable energy sources (RESs) into microgrid (MG) systems have recently demonstrated a significant increase due to the capability of RESs to meet the rising power demands and reduce environmental pollution. However, the fast expansion of MGs and their complex structures due to the wide integration area of distributed RESs and their intermittent behaviour may affect the power system stability and security. Thus, a properly optimised control method is important to guarantee an efficient, secure and high-quality power transfer. This paper highlights a comprehensive study of optimised controller approaches concerning the RES integration into MGs and their classification in terms of structure, characteristics, operation, constraints, functions, cost, pros and cons. This study focuses on the advanced and conventional optimisation algorithms used in MG applications. The rigorous review shows that current control optimisation strategies can enhance the operation and integration of RESs into MGs. However, further improvement of control optimisation is required to achieve sustainable energy operation and renewables integration in the future. The majority of the existing methods is restricted to simulation analysis; consequently, experimental validation is necessary. The standard requirements concerning the integration of RESs into MGs and their possible improvement and harmonisation are highlighted. This study also focuses on different issues, challenges and constraints related to the integration and control optimisation of RESs as well as recommendations for future research. Overall, this review will hopefully strengthen the efforts towards the development of a reliable renewable integration using efficient optimisation algorithms for future applications. • RESs integration into MG has significant impact meeting power demands. • Optimized controller can guarantee an efficient and high quality power transfer. • Optimized algorithm of RESs integration improves sustainable energy operation. • Issues and challenges of RESs integration and recommendations are highlighted. • This review will strengthen the efforts for future RES integration into MG. [ABSTRACT FROM AUTHOR]

This paper considers the case study of a smart microgrid district at Graciosa Island in the Canary Islands. The smart energy microgrid district consists of several households and a public use building (school) that includes renewable energy sources (photovoltaic), Li-ion batteries for electric energy storage, domestic hot water heaters acting as thermal energy storage, a pool for balancing energy consumption and supplies, and the connection to the electric grid. We have modelled such a problem as a nonlinear mathematical programming model that is linearly approximated using special ordered sets of type 2. The linear approximation is solved using Gurobi optimization software, providing close-to-optimum solutions within an interval of 15 min that allows near-real-time operation of the smart energy district. The obtained results allow advancement of the net-zero energy neighbourhood concept in all the evaluated scenarios within a daily horizon and a positive energy balance in wider horizons. Obviously, these results are obtained in part due to the magnificent insolation conditions of the Canary Islands, but they allow justifying that the appropriate use of renewable energy resources and energy storage systems together with a balancing mechanism at the district level (such as the pool in our case study) may also lead to near-net-zero energy neighbourhoods in other geographical locations. [ABSTRACT FROM AUTHOR]

In developing countries such as in Africa, rural areas are not always connected to the national or regional electric grid. As there is a huge potential for solar energy production, photovoltaics are established to supply electricity for operating critical infrastructures with battery systems and diesel generators installed for redundancy. These systems have the potential for further utilization. As a first step, overproduction of solar energy is offered to households due to simple control rules observing battery levels. This paper proposes an optimization method for energy allocation by process graphs or P-graphs. The method provides preliminary estimations on optimal periodical energy balances between energy producers, storage, and consumers of different priorities. These preliminary estimations serve as reconciliation parameters for advanced real-time control of energy distribution. Each step of transforming the practical problem of multiperiod energy distribution to the language of process network synthesis and then optionally to mathematical programming is detailed. To represent the problem adequately, the original formulation of process network synthesis is extended to take into account potential targets and intermediate entities that cannot be accumulated. The work aims to provide better utilization of renewable energy sources and storage. The microgrid profit and return of investment can also be improved by the proposed methodology. Image 1 [ABSTRACT FROM AUTHOR]