Reconstructing hourly residential electrical load profiles for Renewable Energy Communities using non-intrusive machine learning techniques

The successful implementation of Renewable Energy Communities(RECs)involves maximizing the self-consumption within a community,particularly in regulatory contexts in which shared energy is incentivized.In many countries,the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users(e.g.,residential and commercial users)makes the design of a new energy community a challenging task.This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data(i.e.,billed energy),with the aim of estimating the energy shared by RECs.The proposed methodology involves three phases:first,identifying the typical load patterns of residential users through k-Means clustering,then implementing a Random Forest algorithm,based on monthly energy bills,to identify typical load patterns and,finally,reconstructing the hourly electrical load profile through a data-driven rescaling procedure.The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant.The Normalized Mean Absolute Error(NMAE)and the Normalized Root Mean Squared Error(NRMSE)were evaluated over an entire year and whenever the energy was shared within the REC.The Relative Absolute Error was also measured when estimating the shared energy at both a monthly(MRAE)and at an annual basis.(RAE).A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04%,an NRMSE of 26.17%,and errors of 18.34%and 23.87%during shared energy timeframes,respectively.Furthermore,our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31%and 0.12%,respectively.

Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy system.Local energy communities(LECs)are expected to play a vital role in this context.However,energy scheduling in LECs presents various challenges,including the preservation of customer privacy,adherence to distribution network constraints,and the management of computational burdens.This paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization method.The proposed approach uses the Limitedmemory Broyden–Fletcher–Goldfarb–Shanno(L-BFGS)method,significantly reducing the computational effort required for solving the mixed integer programming(MIP)problem.It incorporates network constraints,evaluates energy losses,and enables community participants to provide ancillary services like a regulation reserve to the grid utility.To assess its robustness and efficiency,the proposed approach is tested on an 84-bus radial distribution network.Results indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.

Community microgrid:an approach towards positive energy community in an urban area of Dhaka,Bangladesh

The pollution and toxic greenhouse gases produced by fossil fuel combustion are troubling as global energy demand continues to rise.To mitigate the consequences of global warming,a transition to sustainable energy sources is necessary.This manuscript presents a feasible community microgrid design in Hazaribagh,Dhaka based on meteorological data that leads to photovoltaic installation on the rooftop of a local community building.This study shows a microgrid design of a system with the lowest cost of energy and a large renewable fraction,which is analysed using the HOMER Pro software.Using real-time data,analysis of the system cost,cost of energy,renewable fraction,unmet load,energy purchased and energy sold is discussed.A suitable case for electrification is also identified and presented for the selected community.The proposed case yields a cost of energy of$0.0357/kWh,which is 52%less than the current tariff rate,with a 70%renewable fraction.This study will provide people in this community with more green energy at a lower cost;in addition,this designed microgrid sells additional energy to the grid to avoid possible power outages.The potential for a positive energy community is also investigated in terms of energy consumption and renewable output of the planned microgrid.

Co-Optimization of Behind-the-Meter and Front-of-Meter Value Streams in Community Batteries

Community batteries(CBs)are emerging to support and even enable energy communities and generally help consumers,especially space-constrained ones,to access potential techno-economic benefits from storage and support local grid decarbonization.However,the economic viability of CB projects is often uncertain.In this regard,typical feasibility studies assess CB value for behind-the-meter(BTM)operation or whole-sale market participation,i.e.,front-of-meter(FOM).This work proposes a novel techno-economic operational framework that allows systematic assessment of the different options and introduces a two-meter architecture that co-optimizes both BTM and FOM benefits.A real CB project application in Australia is used to demonstrate the significant two-meter co-optimization opportunities that could enhance the business case of CB and energy communities by multi-service provision and value stacking.

Optimal Planning of Community Integrated Energy Station Considering Frequency Regulation Service

With the extensive integration of high-penetration renewable energy resources,more fast-response frequency regulation(FR)providers are required to eliminate the impact of uncertainties from loads and distributed generators(DGs)on system security and stability.As a high-quality FR resource,community integrated energy station(CIES)can effectively respond to frequency deviation caused by renewable energy generation,helping to solve the frequency problem of power system.This paper proposes an optimal planning model of CIES considering FR service.First,the model of FR service is established to unify the time scale of FR service and economic operation.Then,an optimal planning model of CIES considering FR service is proposed,with which the revenue of participating in the FR service is obtained under market mechanism.The flexible electricity pricing model is introduced to flatten the peak tieline power of CIES.Case studies are conducted to analyze the annual cost and the revenue of CIES participating in FR service,which suggest that providing ancillary services can bring potential revenue.

Scalable Distributed Optimization Combining Conic Projection and Linear Programming for Energy Community Scheduling

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection(CP) and linear programming(LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

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.

Community Integrated Energy System Trading:A Comprehensive Review

Focused on life,consumption,and leisure,communities have been regarded as the basic unit of energy use in a city owing to rapid urbanization,whose energy use density continues to increase.Moreover,community integrated energy systems(CIESs)in the rapid development stage have become embedded,small,and self-sufficient energy ecosystems within cities because of their environmental and economic benefits.CIESs face a competitive energy trading environment that comprises numerous entities and complicated relationships.This paper presents an extensive review of various issues related to CIES trading.First,the concepts,types,and resources of CIESs are described.Second,the trading patterns and strategies of CIESs are reviewed from the four perspectives of the trading objects:community-to-peer(C2P),peer-to-peer(P2P),community-to-community(C2C),and community-to-grid(C2G).Third,a tri-layer trading framework and the features of CIESs that participate in combined multienergy markets are proposed.Last,the key issues in CIES trading are summarized.

Priority-based Residential Demand Response for Alleviating Crowding in Distribution Systems

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances.Though the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some challenges.The crowding among residential customers is one of such challenges.The scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed limits.In order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy communities.In the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor.The non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena.Since the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global optimum.The proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.

Grid-connected microgrid:design and feasibility analysis for a local community in Bangladesh

Global demand for electricity is growing significantly in developing nations.Renewable energy accounts for barely 3%of total energy consumption in Bangladesh.Sources of renewable energy,e.g.solar,are increasingly being acknowledged as viable supply-side choices for microgrids.This article presents a grid-connected microgrid design based on meteorological data for a local community situated in Mohammadpur,Dhaka.This study presents a feasible design of a system that gives the lowest cost of energy production and emissions that is evaluated using software named Hybrid Optimization Multiple Energy Resources(HOMER Pro).Comparison and assessment of the net present cost,cost of energy,operating cost and environmental emission for five different feasible microgrids are analysed concerning real-time data.Also,a suitable case is sorted out and proposed for the local community for electrification.The proposed case offers a$0.0442/kWh cost of energy,which is~32%cheaper than the current rate with a 57.5%renewable fraction and a payback period of 16.86 years.People of this local community will have access to considerably more clean energy at a lower price by this study;also this design could sell the excess energy to the grid to avoid frequent electricity outages.