Smart energy management system framework for population dynamics modelling and suitable energy trajectories identification in islanded micro-grids

In an increasingly electrified and connected world,renewable energy production and robust distribution as well as sobriety paradigm,both for the individual and the society,will most likely play a central role regarding global systems stability.Consequently,while being able to conceive efficient storage systems coupled with robust energy management strategies present significant interests,a number of related studies often consider the human behaviour factor separately.While not decisive in large industrial factories,human demeanor impact cannot be overlooked in residential areas.As such,this work proposes an innovative and flexible dynamic population model,inspired from epidemiological methods,that allows simulation of a vast spectrum of social scenarios.By pairing this formalization with a smart energy management strategy,a complete framework is proposed.In particular,beyond the theoretical identification of sustainable parameters in a wide diversity of configurations,our experiments demonstrate the relevance of reinforcement learning agents as efficient energy management policies.Depending on the scenario,the trained agent enables an increase of the sustainability areas over baseline strategies up to 200%,thus hinting at ultimately softer societal impact.

Energy storage resources management:Planning,operation,and business model

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads,how to maintain the stable,reliable,and efficient operation of the power system has become a challenging issue requiring investigation.One of the feasible solutions is deploying the energy storage system(ESS)to integrate with the energy system to stabilize it.However,considering the costs and the input/output characteristics of ESS,both theinitial configuration process and the actual operation process require efficient management.This study presents a comprehensive reviewof managing ESs from the perspectives of planning,operation,and business model.First of all,in terms of planning and configuration,it is investigated from capacity planning,location planning,as well as capacity and location combined planning.This process is generally the first step in deploying ESS.Then,it explores operation management of ESS from the perspectives of state assessment and operation optimization.The so-called state assessment refers to the assessment of three aspects:The state of charge(SOC),the state of health(SOH),and the remaining useful life(RUL).The operation optimization includes ESS operation strategy optimization and joint operation optimization.Finally,it discusses the business models of ESS.Traditional business models involve ancillary services and load transfer,while emerging business models include electric vehicle(EV)as energy storage and shared energy storage.

Energy cost minimization through optimization of EV, home and workplace battery storage

Besides grid-to-vehicle(G2 V) and vehicle-to-grid(V2 G) functions, the battery of an electric vehicle(EV) also has the specific feature of mobility. This means that EVs not only have the potential to utilize the storage of cheap electricity for use in high energy price periods, but can also transfer energy from one place to another place. Based on these special features of an EV battery, a new EV energy scheduling method has been developed and is described in this article. The approach is aimed at optimizing the utilization EV energy for EVs that are regularly used in multiple places. The objective is to minimize electricity costs from multiple meter points. This work applies real data in order to analyze the effectiveness of the method. The results show that by applying the control strategy presented in this paper at locations where the EVs are parked, the electricity cost can be reduced without shifting the demand and lowering customer's satisfaction. The effects of PV size and number of EVs on our model are also analyzed in this paper. This model has the potential to be used by energy system designers as a new perspective to determine optimal sizes of generators or storage devices in energy systems.

A new hybrid AI optimal management method for renewable energycommunities

In this study, we propose a hybrid AI optimal method to improve the efficiency of energy managementin a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Networkto forecast the future values of the energy features in the community. Then, these forecasts are used by astochastic Model Predictive Control to optimize the community operations with a proper control strategy ofBattery Energy Storage System. The results of the predictions performed on a public dataset with a predictionhorizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation,total energy consumption, and common services, respectively. The model predictive control fed with suchpredictions generates maximum income compared to the competitors. The total income is increased by 18.72%compared to utilizing the same management system without exploiting predictions from a forecasting method.

Ocean energy applications for coastal communities with artificial intelligence–a state-of-the-art review

Ocean energy plays essential roles in reducing carbon emission and transforming towards carbon neutrality, with cleaner power production, whereas the vertical cascade ocean energy systems with spatiotemporal power supply characteristics might lead to fluctuated power frequency, disruptive disturbance and grid shock. Hybrid renewable energy dispatch, coordinated demand-side management, and electrical energy storages for grid ancillary services provision with different response time-durations are effective solutions to integrate ocean energy with stable and grid-friendly operation. This study is to review advanced ocean energy converters with thermodynamic, hydrodynamic, aerodynamic, and mechanical principles. Power supply characteristics from multi-diversified ocean energy resources are analysed, with intermittency, fluctuation, and spatiotemporal uneven distribution. Hybrid ocean energy storages with synergies are reviewed to overcome the intermittency and provide grid ancillary services, including pumped hydroelectric energy storage, ocean compressed air energy storage, and ocean hydrogen-based storage in different response time durations. Applications of diversified ocean energy systems for coastal residential communities are reviewed, with energy management and controls, collaboration on multi-carrier energy networks. Furthermore, application of artificial intelligence is reviewed for sustainable and smart ocean energy systems. Results indicated that, effective strategies for stable and gridfriendly operations mainly include complementary hybrid renewable system integrations, synergies on hybrid thermal/electrical storages, and collaboration on multi-carrier energy networks. Furthermore, depending on the geographical location, flexible on-shore and off-shore installation of transformers can provide large-scale ocean energy system integrations for long-distance transmission, with low transmission losses, low resistive losses, and simple system configuration. Research results can provide a heuristic overview on ocean energy integration in smart energy systems, providing alternatives for solar and wind energy resources and paving path for the carbonneutrality transition.