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• A new home energy management tool considering negawatt trading is developed. • A novel algorithm is developed to convert the problem into a negawatt-aware tool. • Conventional uncertainties are modelled via scenarios. • Electricity bill is reduced by 45% if negawatt opportunities are optimally exploited. • Negawatts exported is incremented by 35% if storage capacity increases by 2 kWh. The decarbonization and sustainability of smart cities is calling for new schemes and businesses capable of increasing the efficiency of energy systems. In this context, energy communities are emerged as a valuable framework to optimally integrate prosumers into electricity networks and take the maximum advantage of domestic resources such as controllable appliances or rooftop photovoltaic arrays. Within communities, prosumers can exchange energy through peer-to-peer platforms, but they can also exchange negawatts. This trading mechanism focuses on acquiring or selling consumption rights rather than actual energy, thus further incrementing the flexibility of the system. In this regard, this paper develops a novel stochastic-based home energy management model considering negawatt trading. An original iterative algorithm is presented by which the developed negawatt-focus problem is converted into a more useful and practical negawatt-aware mechanism, by which the scheduling plan is ready to exploit trading opportunities without notably incrementing the electricity bill, which is considered the primary objective of home energy management applications. Several results are presented to validate the new tool, illustrate the concept of negawatt trading and how it affects to power allocation in prosumer installations. Results show that negawatt trading supposes a monetary opportunity for prosumers if it is optimally exploited, enabling a reduction of the electricity bill by 45 % compared with the conventional case. Moreover, the role of batteries in negawatt trading is highlighted, providing energy backup when importable power must be reduced. Actually, total negawatts exported increase by 34 % when storage capacity increases from 3 to 5 kWh. [ABSTRACT FROM AUTHOR]

• Developing a new stochastic-interval model for energy management in communities. • The developed model considers controllable demand, vehicle-to-grid and energy storage. • The developed model allows to adopt pessimistic or optimistic strategies. • Obtained results reveal the benefits on gathering prosumers on communities. • Community operation reduces the energy that must be acquired from the grid. With the advent of smart cities, residential consumers have evolved towards the concept of prosumers. This paradigm calls for new businesses like energy communities. Energy management of such frameworks is essential to maximize the collective welfare. This paper addresses this issue by developing an energy management framework that accounts for flexible demand, storage devices and electric vehicles. Its two-level structure allows to address the energy exchanging among prosumers in the 1st stage, while the 2nd stage focuses on optimally schedule the different collective assets. A novel stochastic-interval model is proposed to handle with uncertainties from demand, renewable generation, vehicles' behaviour and energy pricing. A case study is performed on a six-prosumer community. Results serve to validate the new tool and analyse the importance of smart devices. In addition, the new proposal allows to adopt different operating strategies. Actually, the total operating cost may increase by 90% if a pessimistic point of view is adopted while the importance of the vehicle-to-grid capability is marginal otherwise. Furthermore, the energy purchased could be reduced by 7% when adopting a community arrangement, supposing an improvement in the economy and environmental indicators of the network. Other relevant aspects are identified and discussed in depth. [ABSTRACT FROM AUTHOR]

This paper provides a novel application for the Archimedes wave swing (AWS) device that converts the sea waves into electrical energy using a linear permanent magnet synchronous generator (LPMSG), which is connected to a rectifier that extracts the most significant energy from sea waves and reduces the stator losses. The generator's power combined with a photovoltaic (PV) system provides a total of 250 kW, sufficient for feeding a V3 Tesla Supercharging system, which is currently the latest and most advanced electric vehicle (EV) charging technology. The hybrid system is connected to a DC link, its voltage is stabilized using a supercapacitor energy storage system, and the output is provided to a buck converter that reduces the voltage for the Tesla Supercharger. The control system comprises seven proportional-integral (PI) controllers with four anti-wind back-calculation coefficients to improve transient stability. The PI gains are optimized using the Golden Jackal Optimization Algorithm (GJOA), and the system stability is evaluated by applying different disturbances like a sudden variation in the DC load, temporary DC short circuit, and connection of the EV in various modes such as a grid to vehicle and vehicle to grid modes. The results obtained by the GJOA are compared with the Particle Swarm Optimization and the hybrid Augmented Grey Wolf Optimizer and Cuckoo Search algorithms. Finally, hybrid renewable energy systems can efficiently be employed in EV supercharging stations as validated with the strategy proposed in this work. • A hybrid wave and PV system is proposed. • A nonlinear model of the AWS wave energy system is presented. • A novel optimal control based on the GJOA is described. • The proposed model is compared with other recent models. • The effectiveness of the proposed model is verified under different disturbances. [ABSTRACT FROM AUTHOR]

This paper introduces a novel enhancement to the Transient Search Optimization (TSO) algorithm to estimate an accurate electrical model of the proton exchange membrane fuel cell (PEMFC). The PEMEFC model is a non-linear model that includes seven unknown variables which cannot be calculated analytically. The TSO is enhanced by inserting two new factors, the Levy function and the Weibull distribution function. The proposed enhanced Transient Search Optimization (ETSO) and TSO algorithms are applied to estimate the seven variables by minimizing the sum of the squared errors (SSEs) between the measured and calculated voltages. The error is defined as the difference between the measured and the calculated output voltage of the PEMFC. Three different commercial types of PEMFCs are modeled: i) Ballard, Mark V 5 kW, ii) Horizon H-12, and iii) 6 kW Nedstack PS6 stacks PEMFC. The estimated seven variables and the minimum SSE of electrical PEMFCs using ETSO and TSO algorithms are compared with the results obtained by using other optimization algorithms like whale optimization algorithm, genetic algorithm, neural network algorithm and others. The results obtained by the ETSO are better than that obtained by TSO by more than 10% and this percentage increases with other algorithms. The accuracy of the proposed PEMFC model is verified by comparing the estimated V – I and P – I characteristics with the measured data. The effectiveness of the proposed ETSO based model is verified by an investigation of sensitivity analysis for design variables and the robustness of the ETSO algorithm via the statistical analysis and the parametric t -test. • This paper presents a novel improved TSO algorithm to extract PEMFC model parameters. • Three commercial PEMFC models are implemented in this paper. • The parameters of proposed model are compared with other recent models. • The proposed model is verified by comparing its results with the measured results. • The sensitivity and robustness analyses of the proposed method are tested. [ABSTRACT FROM AUTHOR]

Decarbonization of the energy sector calls for new energy sources and power-system operation paradigms. In this context, consumers can partake in system operation as loads and generators by leveraging own resources, such as rooftop photovoltaics (PVs). These prosumers can be gathered into energy communities (ECs) and operated in a centralized manner to exploit eventual synergies optimally. The ECs are usually connected to a distribution network and thus interact with the distribution grid operator (DSO), complicating the scheduling strategies under agent interactions. Moreover, the DSO can thus manage his own resources such as distributed generators or medium-scale storage systems. The operation becomes more complex due to multi-source uncertainties in ECs and DSO. This paper addresses this issue by developing a novel bi-level energy management framework for the robust optimal coordination of ECs and DSO against uncertainties. In this framework, the upper-level agents provide the information by solving the designed optimization problems for the EC operation, sending it to the lower-level agents to facilitate the optimal scheduling of distributed assets. In particular, a novel robust reformulation of the lower level problem is proposed, to handle with uncertainties in renewable generation, demands, and energy prices fixed by the local retailer. The resulting formulation is mixed-integer-linear programming that is efficiently manageable by common solvers. Simulation results on the standard IEEE 33-bus distribution system with six connected ECs and various distributed assets (e.g., renewable generators and storage systems) demonstrate the effectiveness and robustness of the proposed framework. The results also evidence the vital role of storage systems in maximizing the DSO's profits while reducing the ECs' operating costs. [ABSTRACT FROM AUTHOR]

• A novel optimal FLC is presented for performance enhancement of microgrids. • A detailed model of microgrids and its real-time implementation is introduced. • The optimal FLC is based on the AVOA strategy. • The proposed model is compared with other recent models. • The effectiveness of the proposed model is verified under different disturbances. Distributed power grid systems require more intelligent and adaptable control management and optimization to preserve the best performance. These balance the generated power source and load demand even during severe interruptions. This research proposes a comparative analysis of an autonomous microgrid system optimized by fuzzy logic control and proportional-integral controller. The microgrid fuzzy logic control parameters are designed using the African vulture and Particle swarm optimization algorithms. At the same time, the proportional-integral controller is designed by using the Gorilla troops optimization algorithm. The suggested microgrid control techniques are verified using a MATLAB/Simulink environment. The overall system's detailed modeling and control methodologies are also presented. The investigation of the fuzzy logic control approach is also tested in a real-time setting. It is implemented by using OPAL real-time 4510 rapid control prototyping. The attained results prove the superiority of the fuzzy logic controller based on the African vulture optimization technique compared with other optimal control techniques. It is applied to give an ideal design for the fuzzy logic control parameters to preserve adequate power to the different loads. The proposed optimal controller is able to bring a robust and effective control with an accuracy of approximately 4%, 5%, 28%, and 26% than other techniques under different scenarios. Index Terms: African Vultures Optimization algorithm (AVOA), Autonomous Microgrid, Decentralized energy generation, Fuzzy Logic Control, Opal RT-LAB, Proportional plus integral controller. [ABSTRACT FROM AUTHOR]

• A new price setting mechanism for microgrid clusters is developed. • The new approach only requires sharing boundary information. • Uncertainties are incorporated using interval notation and equivalent scenarios. • The novel proposal encourages the participation in energy trading. • The strategy adopted by agents notably impact on the energy transacted. With the advance of communication infrastructures and the necessity of increasing the efficiency of energy systems, electricity networks are evolved towards more decentralized architectures and operational schemes. In this context, microgrid clusters are emerging as a valuable framework for optimal integrating renewable sources, demand response initiatives, and storage systems. When the cluster is competitive, the different agents partaking in the cluster compete for trading energy with others, for which an upstream agent (coordinator) sets nodal prices in a similar way to conventional energy markets. This paper is focused on this aspect by developing a novel price setting mechanism for islanded microgrid clusters based on an original equilibrium problem with an equilibrium constraints structure. The new proposal concerns about privacy issues, for which an original diagonalization algorithm is proposed by which only boundary information is transferred from microgrids to the coordinator. Moreover, uncertainties from renewable generation and demand are accommodated using interval notation and equivalent scenarios. The overall problem is raised as a bi-level model, which is further linearized and transformed into a single-level framework tractable by off-the-shelf solvers. A 4-microgrid cluster integrated into a 9-bus network serves as an illustrative case study. The impact of uncertainties is profusely studied, showing that total energy exchanged among microgrids can decrease by 61 % when the influence of uncertain parameters is notable. Other relevant aspects are discussed, and the practicability of the new proposal is demonstrated by analysing its computational performance. Moreover, the developed tool is further validated on a medium-scale network involving a large number of microgrids. [ABSTRACT FROM AUTHOR]

• An optimal bidding strategy for microgrids in clusters is developed. • A multi-stage framework is proposed for considering response fatigue. • Uncertainty risk is managed by enforcing the CVaR. • The total number of response signals can be reduced by 76 %. • The response fatigue minization may reduce the collective welfare by 18 %. The massive integration of renewable generators, energy storage systems, and demand response requires the development of smart power infrastructures. In this upcoming context, microgrids will be essential for the optimal integration of such assets. When different microgrids are located near each other, they can be centrally coordinated within a novel paradigm called Microgrid Cluster. In such structures, the microgrids involved can collaborate in a cooperative way or compete by developing internal market structures. This paper develops a novel optimal bidding strategy for a demand response-intensive microgrid partaking in competitive clusters. The new proposal is envisaged as a three-stage methodology that aims at reducing the effects of response fatigue. Uncertainties related to inflexible demand and renewable generation are modeled via scenarios, while the risk associated with uncertain parameters is handled by enforcing the Conditional Value at Risk. The resulting computational tool is effective and tractable, as shown in the results obtained on a benchmark three-microgrids cluster. Indeed, the developed methodology is able to reduce the total response signals by 88 % in some cases. Moreover, this case study allows analyzing the effect of response fatigue minimization in the overall cluster performance, showing that the collective welfare can be reduced by 32 % when response fatigue is taken into account. [ABSTRACT FROM AUTHOR]

The integration of Electric Vehicles (EVs) and Photovoltaics (PVs) into the electric power distribution system is rising worldwide due to the environmental concerns and the rapid depletion of fossil fuels. However, the simultaneous integration of EVs and PVs in the power system poses challenges of uncertain power penetration, charging, and dynamic loading conditions, which requires a suitable control design and scheduling optimization to keep the modified electric system stable. This paper proposes a solar-based grid-tied charging station (SGTCS) that optimizes EV charging by enabling the scheduling technique resulting in maximum utilization of PV power. The proposed model also identifies and considers seasonal variation effects of power generation and EV charging connectivity. The SGTCS is implemented using HOMER Grid, and a case study of a practical locality in Islamabad was investigated; the conducted detailed analysis comprises the annual cost estimation, energy costs, EV scheduling, main grid power usage reduction, and the impact of environmental changes. Examining the integration of small and big EVs in the grid-tied solar-integrated charging system, a brief scheduling strategy of 250 sessions per day is analyzed. The resultant techno-economic implications are presented, depicting the proposed study's relevance and applicability. • This paper presents an optimal scheduling and techno-economic analysis of EVs. • This is based on solar-based grid-tied charging station. • A case study of a practical locality in Islamabad is investigated. • The model considers seasonal variation effects of power generation and EV charging. • The results of proposed model are compared with other models. [ABSTRACT FROM AUTHOR]

Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand. • A novel stochastic-interval model for robust scheduling of PVaCSs is developed. • A comprehensive stochastic model for multi-mode EV charging is presented. • A multi-stage solution procedure for the developed model is described. • The new proposal allows to consider optimistic and pessimistic strategies. • A case study validates the developed formulation and modelling. [ABSTRACT FROM AUTHOR]

This paper introduces an enhancement to the transient stability of a wave energy conversion system (WECS) by using the coot optimization algorithm (COA) combined with an anti-windup method. This combination helps in elimination of the windup issue in the integral term of the proportional-integral (PI) controller during power system faults leading to a significant enhancement for the transient stability of the WECS connected to the grid. The COA is utilized to select the PI controller parameters and the anti-windup method back-calculation coefficients. The WECS converts the linear vertical motion into electrical energy by using an Archimedes wave swing device connected to a linear synchronous generator. Minimization of the generator's stator losses and maximization of the generator's real power is accomplished by the utilization of a generator-side converter (GSC). Also, both the point of common coupling voltage and the capacitor link voltage are set at their reference values by utilizing a grid-side inverter (GSI). An optimal design for the PI controllers in both converters is achieved by direct application of the COA to the MATLAB/Simulink model. A comparison is made among the results obtained by the COA and those obtained by other recent optimization algorithms under different grid fault conditions. [Display omitted] • A detailed model of the WECS is presented comprehensively. • Enhancement of the transient performance of grid-connected WECS is the main goal. • A new optimization algorithm is applied to select the parameters of the PI controllers and the anti-windup coefficients. • Different scenarios have been investigated under normal and abnormal operating conditions. • The results obtained validate the robustness of the proposed controllers. [ABSTRACT FROM AUTHOR]

• A novel uncertainty-aware day-ahead scheduling tool for microgrids considering response fatigue is developed. • The novel methodology presents a modular and linear formulation, being thereby solvable by conventional solvers. • Various types of demand response programs are modelled and analysed. • Uncertainties and response fatigue are jointly treated by an original information gap decision theory-based methodology. • A benchmark case study serves to show the capabilities and effectiveness of the developed method. The implantation of demand response programs may be unsuccessful due to a variety of reasons. One of the most important is the so-called response fatigue, which refers to the discouragement experienced by consumers when they received an excessive number of signals from the operator. This circumstance is, however, typically ignored in energy management tools of electrical energy systems. To solve this issue, this paper proposes an uncertainty-aware day-ahead optimal scheduling tool for grid-connected microgrids based on information gap decision theory, which incorporates additional constraints to bound the duration of demand response signals. Thereby, the harmful effects caused by response fatigue are lessened. The developed optimization problem is formulated as a Mixed-Integer-Linear programming, which is solvable using standard solvers and versatile enough to be adapted to different system layouts. A benchmark case study serves to show the effectiveness of the developed methodology to manage with uncertainties, while the effect of response fatigue in consumers is bounded to acceptable thresholds. As a sake of example, the developed methodology is able to determine a scheduling plan with a total renewable generation 50% lower compared with the deterministic case, while the total demand is overestimated by ∼20%, in which the effect of response fatigue is kept within acceptable bounds yet. Accurateness and efficiency of the new proposal are also checked by making a comparison with other uncertainties modelling. [ABSTRACT FROM AUTHOR]