Peak-to-average Ratio Research Articles

A real-time demand-side management system considering user preference with adaptive deep Q learning in home area network

With the increase in global energy consumption, the demand-side management (DSM) system has grown into an important research topic because of its ability to reduce the total electricity cost and peak-to-average ratio (PAR) by rescheduling loads. Besides, the large amount of sensor data in the home area network (HAN) helps to record the energy demand and achieve the DSM capacity, and the machine learning skills such as reinforcement learning can be applied to solve the DSM problem. However, determining a suitable energy management strategy is complicated because the user behaviors are uncertain. In this study, a real-time multi-agent DSM system based on HAN was proposed to find a suitable control policy for reducing the energy cost in a smart home. This system integrated the Deep Q-Network (DQN) agents that adaptively learn the preference of appliance usage to control different types of appliances and energy storage system. The simulation results show that the proposed DSM system reduced peak value, PAR value, and electricity cost by 28.9%, 20.9%, and 28.6% respectively. This system can also be applied to REDD dataset and achieved 74.9% cost reduction.

Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches

Smart grid technology has given users the ability to regulate their home energy use more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduce a metaheuristic-based HEM system which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on the EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB were used to test the performance of the proposed hybrid technique. The simulations were run for multiple homes with multiple appliances, which were categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques helped us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques efficiently reduced and shifted the load from peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performed better in terms of cost reduction. In comparison to EWA and HSA, HSA was more efficient in terms of PAR. However, the proposed hybrid approach EHSA gave the maximum reduction in cost which was 2.668%, 2.247%, and 2.535% in the case of 10, 30, and 50 homes, respectively, as compared to EWA and HSA.

Demand-side management and its impact on utility and consumers through a game theoretic approach

The modern smart grid facilitate improved demand flexibility, but the self-optimizing nature of participating users will impact the effectiveness and stability of the demand-side management (DSM) program. In this article, the DSM is modeled as a non-cooperative game between users. To ensure the effectiveness and stability of the solution, the existence of Nash equilibrium (NE), its quality and multiplicity are thoroughly investigated for the system model consisting home appliances, distributed energy storage (DES) and dispatchable generation (DPG). The payoff function of the formulated game is not strictly convex due to addition of DES and DPG. Therefore, the proximal decomposition algorithm (PDA) is utilized to get the NE of the game. The convergence of PDA has a strong dependency on system parameters. A rigorous mathematical proof is provided for the condition of convergence of the PDA. The formulated DSM model has been utilized for analyzing its impact on the users as well as distribution utility. In the case of former, its impact on the user’s energy bill, profile of DES and DPG, comfort and privacy concern are investigated. In latter case, its impact on system performance parameters such as system peak-to-average ratio (PAR), bus voltage, total system loss, bus voltage deviation index, voltage stability indices, and system reliability parameters are studied. The IEEE-33 bus distribution system is considered for the assessment of the proposed study. The obtained result shows a significant reduction in the energy bill and PAR and improved system performance parameters.

An optimal home energy management system with integration of renewable energy and energy storage with home to grid capability

Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home-to-grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low-price period, the ESS and EV are charged and discharged during the high-price period. The paper built a multi-objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi-objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits.

Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid

With the smart grid development, the modern electricity market is reformatted, where residential consumers can actively participate in the demand response (DR) program to balance demand with generation. However, lack of user knowledge is a challenging issue in responding to DR incentive signals. Thus, an Energy Management Controller (EMC) emerged that automatically respond to DR signal and solve energy management problem. On this note, in this work, a hybrid algorithm of Enhanced Differential Evolution (EDE) and Genetic Algorithm (GA) is developed, namely EDGE. The EMC is programmed based with EDGE algorithm to automatically respond to DR signals to solve energy management problems via scheduling three types of household load: interruptible, non-interruptible, and hybrid. The EDGE algorithm has critical features of both algorithms (GA and EDE), enabling the EMC to generate an optimal schedule of household load to reduce energy expense, carbon emission, Peak to Average Ratio (PAR), and user discomfort. To validate the proposed EDGE algorithm, simulations are conducted compared to the existing algorithms like Binary Particle Swarm Optimization (BPSO), GA, Wind Driven Optimization (WDO), and EDE. Results illustrate that the proposed EDGE algorithm outperforms benchmark algorithms in energy expense minimization, carbon emission minimization, PAR alleviation, and user discomfort maximization.

Managing the Demand in a Micro Grid Based on Load Shifting with Controllable Devices Using Hybrid WFS2ACSO Technique

Demand Side Management (DSM) is an effective tool for utilities through reducing the demand of peak load and controlling the utilization of the energy of the system. The implementation of DSM provides benefits for utilities and is profitable for the customers who are involved in this process. DSM based on a load shifting strategy is proposed in this paper by employing various devices to minimize the energy consumption pattern in the system. The proposed hybrid strategy is the joint implementation of the Wingsuit Flying Search Algorithm (WFSA) and Artificial Cell Swarm Optimization (ACSO). The searching behavior of WFSA is enhanced by ACSO. Hence, it is named the WFS2ACSO technique. The implementation of this load shifting technique was carried out on three different types of loads, these being residential loads, commercial loads, and industrial loads. Two case studies, over summer and winter, were validated to check the feasibility of the test system. The proposed method aimed to achieve the load demand in an effective way for the minimization of bill electrification, Peak to Average Ratio (PAR), and the consumption of power. The Time-of-Use (TOU) pricing was implemented to calculate the savings in energy bills. The proposed test system of the Micro Grid (MG) was executed on a MATLAB platform with two case studies based on the optimization methods WFSA and WFS2ACSO. Simulation results demonstrated the comparative analysis of electricity cost and peak load with different algorithms and were carried out with and without DSM consideration. The projected DSM methodology achieved considerable savings as the peak load demand of MG decreased. Furthermore, the decrease in PAR levels of 14% in the residential load, 16% in the commercial load, and 10% in the industrial load, with and without the DSM methodology, was presented. The flight length and awareness of probability tuning parameters make the proposed algorithm more effective in obtaining better results. The test results obtained prove the effectiveness of the hybridized algorithm as compared with other trend-setting optimization techniques such as Particle Swarm Optimization (PSO) and Ant Lion Optimization (ALO).

A Modified Coronavirus Herd Immunity Optimizer for the Power Scheduling Problem

The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios.

Optimality and PAR Reduction in Autonomous Demand Response: Evaluation and Billing Mechanisms

The main objective of this paper is to propose a more efficient, distributed, and multi-objective billing model that can be implemented in every smart meter in the grid for achieving optimality and fairness. First, we develop a new evaluation index to evaluate such as billing model not only in addressing fairness but also to minimize the cost and reduce the Peak-to-Average Ratio (PAR) in the load demand and subsequently the bill of each customer. Then, we study some of the billing models that exist in the literature and evaluate them with our evaluation index. Simulations are performed to test the performance of our model in terms of optimality, PAR reduction and fairness.

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO2 in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user comfort improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in terms of average delay, indoor air quality, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy bill costs, reduce carbon emissions, mitigate peak loads, and improve user comfort.

Optimal Planning of Residential Microgrids Based on Multiple Demand Response Programs Using ABC Algorithm

The smart grid has revolutionized the conventional electricity grid with the proposition of demand-side management (DSM). A DSM program enables the user to schedule its energy consumption in compliance with any pricing signal. This scheduling helps the grid operator to reduce the peak load demand and jointly benefits the user to reduce its electricity costs. Despite that, while doing so, it jeopardizes the user’s comfort. In the present paper, the authors have investigated the impact of communal DSM programs on the consumption patterns of users, including single as well as multiple households. The objective is to simultaneously minimize the electricity costs and user discomfort to make a win-win situation for both the grid operator and the user. Therefore, a multi-objective optimization problem (MOOP) has been formed to simultaneously minimize the daily electricity cost, peak to average ratio (PAR) of load demand, user discomfort, environmental emission, and total net present cost (TNPC). In order to evaluate the best scheduling method, sizing scenarios for a residential microgrid in a Southern Pakistani metropolis surrounded by rural areas are presented in this paper. The originality of this article comes from a comparison of the techno-economic and environmental performance of several sizing options for a residential load powered by renewable energy. The artificial bee colony (ABC) algorithm has been selected to solve the MOOP. The DSM programs are based upon different pricing signals, including real-time electricity pricing (RTEP), critical peak pricing (CPP), time of use (TOU), and day-ahead pricing (DAP) pricing. The results of the proposed ABC algorithm are compared with GA and standard algorithms, and they reveal the effectiveness of the proposed method. When demand response is used, the suggested optimization technique shows that the SH spring with PV/WG/grid-connected microgrid is the most investable-reliable sizing option with a minimal TNPC of $1405.18 for DAP tariff with SH spring. Additionally, with a reduction in emissions of 6699 kg/yr, DAP tariff with SH spring shows that PV/WG/DG/grid-connected system has the greatest impact on the environment. For DAP tariff with SH spring, optimal sizes of PV, WG and converter are 26.7 kW, 30 kW and 6.67 kW, respectively.

Investigation on Optimization Algorithms for Smart Home Energy Management with Different Electricity Pricing

The focus of this paper is on energy consumption optimization in smart homes (with/without RES) and increasing the user comfort level. The paper presents a functional and adaptable home energy management system with RES and an energy storage device for designing and implementing Demand Response (DR) programs. The four meta-heuristic techniques: Genetic Algorithm (GA), Wind-Driven Optimization (WDO), Grey Wolf Optimization (GWO) and Salp Swarm Optimization (SSA), are used to optimize the energy consumption cost for a home energy environment. In the process of identifying and proposing a dedicated home energy optimization algorithm, this paper investigated four optimization algorithms with four different pricing schemes: Time of Use (TOU) pricing, Real-Time Pricing (RTP), Critical Peak Pricing (CPP), and Day-Ahead Pricing (DAP) schemes. The results obtained using these pricing schemes are validated and compared in a common smart home environment. Further, the results show that by integrating Renewable Energy Sources (RES) and a battery reduces the electricity bill by 10.89% (without RES) and 38.88% (with RES), as well as the peak-to-average ratio (PAR) by 59.97% (without RES) and 64.98% (with RES) when compared to the energy consumption cost obtained without-scheduling technique. Moreover, without RES, the SSA algorithm based home energy management system outperforms the other algorithms particularly with the TOU pricing scheme.

MIMO radar robust waveform-filter design for extended targets based on Lagrangian duality

We address the robust waveform-filter design problem for extended targets with a colocated Multiple-Input Multiple-Output (MIMO) radar . The goal is to maximize the worst-case Signal-to-Interference-pulse-Noise Ratio (SINR) at the receiver against the uncertain Target Impulse Response (TIR) with the Peak-to-Average Ratio (PAR) constraint imposed on the waveform, which results in a non-convex minimax optimization problem. Two kinds of uncertainty sets for the TIR, namely, the spherical set and the annular set, are considered. Combing the duality theory in optimization and the Semi-Definite Relaxation (SDR) technique, we devise the Lagrangian Duality Semi-Definite Relaxation (LDSDR) and the Lagrangian Duality Double SemiDefinite Relaxation (LDDSDR) algorithms to tackle the associated waveform-filter design problems against the spherical and the annular sets, respectively. The convergences of the proposed algorithms are proved theoretically. Numerical results verify the effectiveness of the proposed algorithms. Compared with the current algorithms for the spherical set, the proposed LDSDR algorithm achieves the highest worst-case SINR with a reduced computational complexity

A Data-Driven, Distributed Game-Theoretic Transactional Control Approach for Hierarchical Demand Response

Modern power systems require flexible demand-side resources to maintain the balance between electricity supply and demand. Building thermostatically controlled loads (TCLs) are great flexible assets, as they account for a significant portion of electricity consumption in buildings. In this regard, demand response (DR) programs have long been used by grid operators to enable the coordination of TCLs to reveal and utilize this demand flexibility. Existing efforts in DR are mostly model-based, which is not scalable because gathering the physical parameters and developing an accurate model for each participating load is impractical. To address this limitation, this paper proposes a data-driven, distributed hierarchical transactional control approach, which leverages concepts from machine learning, game theory, and model-free control. In this approach, the interactions between a distribution system operator (DSO) and load aggregators (LAs) in the upper level are designed as a Stackelberg game, where the DSO is the leader and the LAs are the followers. The DSO aims to maximize its profit and minimize the total demand peak-to-average ratio (PAR) of the system, whereas the LAs aim to minimize their electricity cost while maintaining the quality of service provided. In the lower level, the LAs control the individual loads of end users while tracking the optimal aggregated load profile passed from the upper level. The performance of this approach is evaluated using a case study with a single DSO, three LAs, and 300 TCLs. The results show that the total demand PAR is reduced from 1,38 to the range of 1.17 and 1.26. The cost of an LA is reduced from $5,790 to the range of 4,424 and $5,437 while still maintaining the comfort of end users, but the profit of the DSO is reduced from $13,374 to the range of $10,084 and $12,904.

Probabilistically Robust Radar Waveform Design for Extended Target Detection

We address radar waveform design problem for extended targets under the Peak-to-Average Ratio (PAR) constraint. Based on the stochastic model of the Target Impulse Response (TIR), the distribution of output Signal-to-Interference-pulse-Noise Ratio (SINR) is derived. After that, a novel waveform design metric, named Probabilistically Robust Detection (PRD), is proposed to improve the stability of detection performance against the stochastic TIR. The geometric and limit interpretations for the PRD metric are given to show its rationality. Leveraging on the Semi-Definite Relaxation schemes and the Dinkelbach's algorithm, we devise algorithms to optimize waveform covariance matrix based on the proposed metric in high and low SINR situations, respectively. It is theoretically proved that the global optimal waveform covariance matrix can be achieved in high SINR situations. On the contrary, only the sub-optimal waveform covariance matrix can be obtained in low SINR situations. The transmit waveform is synthesized from the optimized waveform covariance matrix through randomization schemes. Numerical experiments are carried out to evaluate the PRD metric as well as the proposed algorithms. Results verify the effectiveness of the proposed algorithms. Compared with the current design metric, the proposed PRD metric exhibits strong probabilistic robustness on detecting extended targets.

Integrated Waveform Design for MIMO Radar and Communication via Spatio-Spectral Modulation

This paper considers the integrated waveform design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple-Input Multiple-Output (MIMO) system. To this end, a spatio-spectral modulation strategy via shaping the spatial waveform Energy Spectral Density (ESD) in directions of communication is proposed for the communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, Peak-to-Average Ratio (PAR) and power restrictions to comply with the current hardware technique and the mainlobe width constraint to cohere the beampattern main energy on the spatial region of interest are forced, respectively. Exploiting an equivalent reformulation of the original non-convex optimization problem, a Sequential Block Enhancement (SBE) framework that alternately updates each waveform in each emitting antenna is developed to monotonically decrease ISL. Each block involves the Dinkelbach’s procedure, sequential convex approximation and Alternating Direction Method of Multipliers (ADMM) to obtain single waveform, while the analytic proof with the converged block being a Karush-Kuhn-Tucker (KKT) point is provided. Finally, numerical results highlight the effectiveness of both the proposed dual function scheme and the waveform synthesis technique in comparison with some counterparts.

Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability

In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

Optimizing energy consumption patterns of smart home based on Sine Cosine Algorithm

This paper presents a new application of the sine cosine algorithm (SCA) to obtain optimal home energy management systems (HEMS) that use the load shifting strategy of demand side management (DSM) to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to decrease electricity bills and peak to average ratio (PAR) while maintaining user comfort through coordination among home devices. In order to meet the load demand of electricity consumers, this study schedules the load on a day-ahead and real-time basis. The main objective of the paper is to help balance the load through ON-peak hours and OFF-peak hours. Three price signals, namely: real-time pricing (RTP), time of use (TOU), and critical peak pricing (CPP), are used to evaluate the proposed algorithm. The results show that the electricity bill and PAR are minimized by up to 40% and 50%, respectively. The scheduling using coordination between devices in real-time does not significantly impact the cost of electricity and the peak to average ratio.

Aligning the interests of prosumers and utilities through a two-step demand-response approach

Demand-side management solutions reward flexible customers for achieving desired goals. However, under price-based demand-response programs, uncoordinated load shifting among many customers may lead to rebound peaks, thus incurring financial costs on the energy service provider (ESP). This study addresses this issue from both the ESP (utility company) and customer’s perspectives in a two-step approach. First, each customer solves a local multi-objective load scheduling problem. Thereafter, the ESP solves a system-wide demand profile optimization problem. More precisely, we formulate in the first step a multi-objective optimization model to minimize consumption costs and load schedule discomfort while considering multiple energy sources and customer preferences. In the second step, we design an approach that combines Pareto-optimal solutions from all customers and minimizes their aggregate demand profile’s peak-to-average ratio (PAR). Experimental results show that the proposed approach outperforms the equivalent single-objective optimization models, and it can reduce the PAR metric by up to 11%. In other words, the proposed approach successfully improves the ESP’s system-wide demand profile aggregation while reducing customers’ expenses and discomfort.

A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

Data-driven district energy management with surrogate models and deep reinforcement learning

Demand side management at district scale plays a crucial role in the energy transition process, being an ideal candidate to balance the needs of both users and grid, by managing the volatility of renewable sources and increasing energy flexibility. The presented study aims to explore the benefits of a coordinated approach for the energy management of a cluster of buildings to optimise the electrical demand profiles and provide services to the grid without penalising indoor comfort conditions. The proposed methodology makes use of a fully data-driven control scheme which exploits Long Short-Term Memory (LSTM) Neural Networks, and Deep Reinforcement Learning (DRL). A simulation environment is introduced to train a DRL controller to manage the operation of heat pumps and chilled and domestic hot water storage for a cluster of four buildings. LSTM models are trained with synthetic data set created in EnergyPlus and are integrated into simulation environment to evaluate the indoor temperature dynamics in each building. The developed DRL controller is tested against a manually optimised Rule Based Controller (RBC). Results show that the DRL algorithm is able to reduce the overall cluster electricity costs, while decreasing the peak energy demand by 23% and the Peak to Average Ratio (PAR) by 20%, without penalizing indoor temperature control.