Microgrid Energy Management System Research Articles

Rule-Based Inferential System for Microgrid Energy Management System

The installation of grid-connected microgrids ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{Gs}}$</tex-math></inline-formula> ) is considered a suitable solution to enhance the modernization of distributed generation systems into smart grids. This realization has raised the need for the development of an energy management system <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$( {{\rm{EMS}}} )$</tex-math></inline-formula> for achieving efficient monitoring, control, and management of the energy flows in the system. In this article, the fuzzy inference system (FIS) based EMS synthesis is proposed to efficiently control the distribution of energy flows of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> in real time. The FIS is further optimized using a genetic algorithm approach to achieve faster evaluation and robust EMS development. In the development process of the EMS, the graph theory-based representation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> s is proposed for efficient representation of the energy demand and energy generation with the energy systems connected in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> . To assess the performance of the proposed EMS, a photovoltaic generation of 19.95 kWp and an aggregated load of 8 kWp are characterized along with a battery energy storage system to form a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> . The results identified the benefits of the proposed approach regarding profit generated and battery usage during the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu {\rm{G}}$</tex-math></inline-formula> operation.

Energy management and control system for microgrid based wind-PV-battery using multi-agent systems

Energy generation is currently evolving into a smart distribution system that incorporates several green energy resources at a distributed level, ensuring that clean energy is generated without releasing harmful gases, that operational procedures are consistent, and that energy management and supervision arrangements are improved. This paper proposes a multi-agent system-based microgrid energy management and proper control in distributed systems. For the complexity of energy management in distributed systems, a multi-agent system-based decentralized control architecture was developed. The proposed technique is based on several smart agents, each agent is based on the microgrid data for energy management and frequency control. The proposed energy management system based on the multi-agent system was tested by simulation under renewable resource fluctuations and seasonal load demand. The simulation results show that the proposed energy management system proved to be more resilient and high-performance controls than conventional centralized energy control systems.

Analysis of a linear programming‐based decision‐making model for microgrid energy management systems with renewable sources

With the increasing share of renewable energy sources in microgrids, systems enhancing the power flexibility at the demand side have become mandatory in microgrid architecture. Thus, the electrical energy storage system has been mostly integrated into microgrids although its capital and operating costs are very high. Hence, diversifying microgrid energy storage based on the end-energy usage can improve the reliability, operating cost, and power demand flexibility of the microgrid. The hereby study integrates an absorption chiller and electrical heat pump coupled to a heat storage into a renewable energy resources-based microgrid and develops its centralized energy management model for both off-grid and on-grid operation modes. The model considers the current energy level of energy storages in the microgrid, current, and forecasted information state to compute the vector-valued decision minimizing a realistic microgrid operating cost. The operating cost includes the energy purchasing/generation, heating/electrical storage, and penalty cost due to load shedding. Hence, such an objective function leads to maximizing the consumption of the generated renewable power, minimizing the energy storage and load shedding cost. The load shedding has been allowed to increase the flexibility in microgrid energy management. However, a high penalty has been imposed on each electrical, heating, or cooling load shedding decision. The decision vector components are thermal and electrical power flow between each energy source to loads and energy storage systems. The problem has been modeled as a Linear Programming with forecasted multi-parametric inputs at different prediction horizons. The effect of the charging/discharging rate and capacity of energy storages, coefficient of performance of absorption chiller and heat pump, prediction horizon, and energy level of storage devices provided to the myopic energy management model has been evaluated for better integration of renewable sources and thermal storage systems into microgrids. Suggestions have been made to improve the microgrid self-consumption, energy efficiency, and myopic decision-making model using the energy level of storage devices obtained from the full look-ahead optimization model. Highlights The optimization problem combining electricity, heating, and cooling is formulated The electrical and thermal storage charging/discharging rate significantly affect the power supplied by the renewable power source decision of the microgrid energy management model. Accurate energy storage level information into the myopic energy management model improves the model decisions. The absorption chiller coefficient of performance greatly impacts the cooling and electrical loads shedding.

Distributed control for polygeneration microgrids: A Dynamic Market Mechanism approach

This paper develops a Dynamic Market Mechanism (μDMM) to optimally allocate electric and thermal power in a combined heat and power microgrid. The market mechanism is formulated as a receding horizon constrained optimization problem, from which an optimal automated transactive procedure is developed. Its operation is distributed in nature and incorporates the most up-to-date electric and thermal load estimates and renewable generation. The results of the market clearing of the proposed microgrid μDMM are the spot prices and schedules for electric and thermal power in addition to non-binding estimates of future prices and set-points. The performance of the μDMM is illustrated using simulation studies of a Combine Heat and Power (CHP) microgrid model based on the Smart Polygeneration Microgrid (SPM) located on the University of Genova Savona Campus. The results show that the μDMM has the ability to adapt to new information via updated forecasts and improve the performance of microgrid operation compared to a centralized method. This in turn makes the μDMM attractive for use in μEMS (microgrid Energy Management Systems) by allowing new energy resources to be added in a plug and play fashion.

Integration of Small Productive Processes Into an Energy Management System for Microgrids

Small productive processes (SPPs) have recently emerged as attractive alternatives to contribute to the socio-economic development of communities, primarily in rural contexts. However, SPPs have a complicated electrical behavior involving the interaction between various types of loads, such as, conventional, and complex ones. Further, the SPPs generally include voltage-dependent loads, which may increase/decrease the load consumption. Thus, these characteristics make the integration of the SPPs into energy management systems (EMSs) for microgrids (MGs) a challenging task. This work proposes a novel integration of the SPPs into an EMS considering the SPPs&#x2019; voltage sensitivity and complexity. For this purpose, we extended a previously proposed multi-zone ZIP load model (EMZ-ZIP) to be integrated into a convex AC multi-nodal EMS approach. The latter was formulated based on invertible nonlinear convex transformations and a binomial approximation method. The associated framework was assessed using a modified 9-bus test system with the characteristics of a low-voltage isolated MG with the integration of an SPP. The results demonstrate that the MG operation exhibits better technical and economic performance when the EMZ-ZIP model is an integral part of the EMS. In addition to an operating cost reduction of approximately 5% when considering voltage dependency, relevant practical advantage in scenarios of work shift and solar radiation variability are also presented. These results were compared with those obtained using other approaches like the time-variant ZIP and the constant power model.

An approach of battery energy management systems for DC micro-grids considering stateflow controller

This paper presents an approach of battery energy management systems for DC micro-grids considering Stateflow controller. In micro-grids, batteries have been the favorite energy storage technology focus on addressing and accelerate United Nations Sustainable Development Goal 7 'Affordable and Clean Energy' as well as contributing to the discussion around the long-term mitigation of climate change. However, their improper or non-effective operations can cause over-current, overvoltage or overcharging/discharging and significantly impact on their lifespan and on the micro-grid reliability. Comparatively to Lithium Ion (Li-ion) battery, most of the technologies present some issues, which can impact negatively on the battery lifetime as well as the average of energy stored in the battery. In case of battery bank, the deep discharge or the premature charge of battery can lead to a reduced lifetime of the storage system. The aim of this study was to develop a battery management system algorithm to control the charging/discharging of a battery bank and to keep its state of charge in the admissible limits to avoid the deep charging/discharging of the battery within a DC micro-grid. The objectives of this proposed work were to develop a control system schemes for the DC microgrid and to develop an energy management system using MATLAB/Simulink software. The results of the scenarios of the developed algorithm have shown that this algorithm could be able to ensure the reliability, the resiliency, the robustness and the proper operation of the battery systems in micro-grids. The results have shown that the initial investment cost could comparatively be lower and could decrease the economic analyze in terms of LCOE.

Energy Management System of Microgrid using Optimization Approach

Microgrid technology can efficiently integrate a new practical way for large-scale application of grid-connected generation of renewable energy. An Energy Management System (EMS) in microgrid, is important for optimum use of the distributed energy resources in smart, protected, consistent, and synchronized ways. This paper discusses the management of Energy Storage System (ESS) connected in a microgrid with a solar array and control the battery discharge and charge operations with converter based on the necessity of the demand. Problem for one-day energy management of microgrid is discussed. This paper focuses on analyzing of heuristic and optimization approach for minimizing total variable electricity prize for clear and cloudy day. The output variables like power of PV, grid, ESS, and loads, grid voltage, ESS state of charge and price graphs are analyzed for each case. The MATLAB software is used for simulating this case study.

Energy management system for DC microgrids

Microgrids are becoming more widespread to decentralise resources and increase the reliability of the electricity system. A microgrid is defined in this paper as a solar power system, a battery bank, wind energy, a super capacitor, and a load demand that are all connected to a common bus via a DC-DC converter and a dual active bridge converter. Out of many renewable energy supplies are supplied by nature, PV power generation has expanded. Solar energy can be effectively utilised if a dynamic control approach is implemented. Using the converters of PV and battery systems (DC-DC and DAB converters) with the MPPT algorithm, the proposed controller can regulate the DC bus voltage and maximise the use of PV power in the micro grid. MATLAB is used to model and simulate the suggested controller in order to validate and evaluate its performance.

A Designed Energy Management System (EMS) for an Off- Grid Residential Microgrid

This paper proposes an Energy Management System (EMS) of an off-grid residential microgrid comprised of a solar photovoltaic array, wind turbine, and a battery-based energy storage system for a residential building in a remote area. The aim is to design a less complex energy management system that addresses energy resources dispatch challenge in a hybrid residential microgrid. It equitably dispatches resources and manages the generated energy to meet the residential load. The management control system algorithm is implemented using the state machine and simulated in MATLAB/Simulink environment for validation. Three scenarios are simulated, they emulate various contingencies scenarios that might rise due to the intermittency nature of the renewable resources. Simulation results show that the proposed energy management system achieves its design objectives. It maintains the power supply and demand balance, through an appropriate scheduling of resources even during contingency due to the intermittence nature of the renewable resources

Frequency Control and Energy Management of Microgrid with Distributed Energy Storage

The purpose of the Energy Management System (EMS) is to ensure a high degree of efficiency, stability, and dependability. And the electrical system should have the ability to adjust to the majority of changes such as renewable energy integration. In some situations, the power generated by distributed power sources is greater than the local demand. This has an effect on the microgrid's stability and frequency. Smart energy management in a microgrid is proposed in this research., consisting of a wind power generator, photovoltaic and storage energy system for frequency regulation, and system balance. The above paper's main contribution is a system for managing energy. based on a clever strategy using MATLAB/Simulink. The results of the simulation and analyses demonstrate that the suggested microgrid energy management system is efficient at balancing energy.

Energy management system of microgrids in grid-tied mode: A hybrid approach

ABSTRACT This paper proposes a proficient hybrid approach based energy management system (EMS) of microgrid (MG) in grid-tied mode. The microgrid connected system like photovoltaic (PV), wind Turbine (WT), and microturbine (MT) battery. The major intention of the proposed approach is “to reduce the cost of electricity and enhancing the power flow (PF) among the source and load side.” The proposed method is the combination of Artificial Neural Network (ANN) and Hybrid Whale Optimization (HWO) approach, hence it is known as ANN-HWO. The necessary load requirement of grid connected microgrid scheme is constantly monitored by ANN. The HWOA develops the optimal combination of the microgrid based on the predicted load demand. Moreover, the two methods for microgrid energy management have been employed to minimize the impact of renewable energy predicting faults. The first method is focused on the various renewable energy sources (RESs) programming to reduction of electricity cost while the MG function. The second method is to balance the PF and minimize the forecasting errors impacts depending upon rule outlined from the scheduled power reference. The proposed technique is activated in MATLAB/Simulink site, and then the efficiency is classified with and without grid of MG structure. The effectiveness can be compared based on cost analysis and power generation of PV, MT, WT, and battery of existing methods. The power generation from the various sources based upon the ANN-HWO and existing methods is evaluated. Also, the total cost of the system also analyzed with different existing methods. The statistical evaluation, like mean, median, and standard deviation is also analyzed. The accuracy, specificity, recall and precision, RMSE, MAPE, MBE and consumption time of ANN-HWO, and existing techniques are also conducted. The mean, median, and standard deviation for 50 numbers of trails of the proposed technique is 1.3116, 1.2931, and 0.0354. The mean, median, and standard deviation for 100 numbers of trails of the proposed technique is 1.3232, 1.2786, and 0.0946. The RMSE, MAPE, MBE, and execution time for 50 numbers of trails of the proposed technique is 7.840, 0.748, 0.9971, and 1.011s. The RMSE, MAPE, MBE,and execution time for 100 numbers of trails of the proposed technique is 5.21, 0.93, 1.93, and 2.005 s. The proposed technique in 100, 200, 500, and 100 numbers of trails, the efficiency is 99.9673%, 99.7890%, 99.89402%, and 99.77879%.

Development of a Platform for Securing Interoperability between Components in a Carbon-Free Island Microgrid Energy Management System

The rigidity of information technology (IT) has been hindering the development of various businesses regarding energy management systems (EMSs) of power networks, although this area has become more diversified, resulting in changes of elements in the systems due to the introduction of renewable energy (RE) and the new energy industry. In order to effectively accommodate these changes, EMSs should be developed in a structure with a standard-based interface, which can secure interoperability between components in the EMS. In previous studies, the common information model (CIM) proposed by IEC TC57 has been utilized for developing EMSs of power networks, but there are gaps between the existing CIM and an information model for the EMSs of carbon-free island microgrids (MGs), which are a newly introduced form of power network covering multiple islands for reducing carbon emissions. This paper proposes a CIM-based software platform for a carbon-free island MG-EMS to efficiently operate the power network and secure interoperability between components in the MG-EMS. Concerning service restoration of the power network, use cases and business objects representing information exchanged between the components in the EMS are derived, and the existing CIM is extended based on the results of the gap analysis in order to provide necessary information on the MG-EMS. The validity of the proposed platform is verified by exchanging payloads between components in the MG-EMS based on the profile extracted from the extended CIM. Furthermore, the performance of the proposed platform regarding data size and speed of data exchange is presented. Based on the case study results, it is concluded that the proposed platform based on the extended CIM can exchange data between the components in the MG-EMS, achieving reasonable data size and speed of data exchange with the help of the interoperability between components in the carbon-free island MG-EMS.

Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization

Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.

Political-Optimizer-Based Energy-Management System for Microgrids

This paper presents an energy-management strategy based on a recently introduced Political Optimizer (PO) for a microgrid installation at Wroclaw University of Science and Technology. The aim of the study is to check the effectiveness of two recently introduced meta-heuristic algorithms at power-system-operations planning. The optimization algorithms were compared with other conventional meta-heuristics wherein performance tests were carried out by minimizing costs in an IEEE 30-bus system. The best performing algorithm was then used to minimize the Levelized Cost of Energy (LCOE) in a microgrid consisting of renewable energy sources such as solar PV panels, a micro-hydro power plant, a fuel cell with a hydrogen storage tank and a Li-ion storage unit.

Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation

To ensure the autonomous power supply in microgrids (MGs) in stand-alone mode while also maintaining stability, energy storage systems (ESSs) and demand-side flexibility can be utilized together. Motivated by this fact, in this study, a scenario-based energy management system (EMS) modelled as a mixed-integer linear programming (MILP) problem is presented by taking the stochastic nature of wind and photovoltaic (PV) sources into account in order to analyze the operational behaviour of MGs and thereby to reduce the network energy losses. Direct load control (DLC) based demand response (DR) program is implemented to the system with the objective of exploiting the remarkable potential of thermostatically controllable appliances (TCAs) for energy reduction while satisfying comfort and operational constraints. Furthermore, a common ESS with a bi-directional power flow facility is incorporated in the proposed structure and electric vehicles (EVs) are employed as an additional flexible load in grid-to-vehicle (G2V) mode. To testify the effectiveness of the proposed optimization algorithm, different case studies are conducted considering diverse scenarios. Moreover, the performance is compared with a deterministic method from the perspective of achieving loss reduction and capturing the uncertainties.

Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid

The integration of demand response programs (DRP) into the microgrid energy management system can enhance the load characteristics by enabling the active participation of consumers to achieve techno-economic benefits. From the microgrid operator's perspective, these DRP can optimize the revenue and accelerate return on investment. The previous research studies do not accurately represent modelling load responsiveness and price elasticities while implementing demand response programs. The accurate assessment of consumer behaviour to market price changes is paramount while modelling the price elasticities. With this viewpoint, a new microgrid energy management problem is proposed with the integration of flexible price elasticity based incentive-driven DRP in this research work. Further, the network flow constraints are considered to evaluate power losses and voltage deviation at each node of the modified IEEE-33 and IEEE-38 bus distribution networks. The comprehensive load model with the combination of linear and nonlinear conservative load responsive models is adopted and derived for each incentive-driven DRP by considering the nonlinear incentive and penalty model. Nine possible demand response scenarios were created, and techno-economic performance indices for each scenario were evaluated. Finally, the Fuzzy Analytic Hierarchy Program is adopted to choose the best alternative based on each techno-economic criterion. The recently reported swarm optimizer Sparrow Search Algorithm is devised for the first time in the microgrid literature to solve the above-proposed microgrid energy management problem. The obtained results are compared with existing metaheuristics to test the algorithm performance.

OPTIMAL DEMAND CHARGE THRESHOLD TUNING FOR ECONOMIC MICROGRID ENERGY MANAGEMENT

Microgrid energy bills often include a demand charge component which is a cost on the maximum power consumption peak that occurred within a billing period, typically a month. To effectively minimize the energy bill in such a context, setting the target demand charge thresholds to be considered by the microgrid energy management system at the beginning of a new billing period is a challenging task. This paper proposes a heuristic to determine such thresholds using a linear programming model based on the site&apos;s energy consumption profile from the previous year. Furthermore, the effectiveness of the obtained values is assessed through a detailed full-year simulation study. Compared to demand charge thresholds obtained from a simpler heuristic, this assessment indicates an additional saving potential of 10% for the microgrid energy bill.

Optimal probabilistic operation management of smart parking lot and renewable sources in microgrid to reduce cost and improve system reliability considering demand response program

In this article, a new probabilistic operation management model for a microgrid (MG) with renewable sources such as solar and wind and also smart parking lots (PLs) is proposed. The model is based on demand response programing (DRP) with considering renewable sources and PLs uncertainties. An innovative scenario-based weighted objective function is proposed for the MG energy management system. The objective function is a combination of MG operation cost and the energy not supplied (ENS) fines for each scenario. Scenarios are weighted according to the probability of their occurrence in the objective function, so that more probable scenarios have a greater impact on the final cost. The generic Monte Carlo simulation (MCS) is used for generating the scenarios. For the proposed energy management model, the reliability index called loss of power supply probability (LPSP) is considered as a constraint. Also, the modified version of lightning search algorithm (LSA) is used due to higher accuracy. The proposed method is tested on the IEEE 33-bus test system. The simulation results indicated the good performance of the proposed method.

Demand response of grid-connected microgrid based on metaheuristic optimization algorithm

ABSTRACT Demand Response Programs (DRPs) have gained the microgrid (MG) operators phenomenal attention for mobilizing the end-users in alleviating the uncertainties associated with renewable energy sources. In this research work, the effective MG energy management system (EMS) in conjunction with price-driven DRPs is proposed to achieve synergistic coordination between the energy providers and consumers and reduce operational costs. The flexible price elasticity model is implemented instead of using the price elasticity models with a preordained constant value like most existing literature. This results in a more realistic characterization of customer responsiveness to energy price changes and promotes the DRPs under a grid-connected MG environment. With this regard, a stochastic day-ahead energy management strategy is proposed to incorporate four distinct DRPs and schedule the MG distributed energy resources. The proposed strategy is verified on a practical 3-feeder MG test system with a majority of 50% industrial load considered. The short-term scheduling period of 15 min ahead solar and wind power forecast is considered to optimize the microgrid dispatch costs accurately. The intermittent nature of renewable energy sources is addressed by employing a stochastic-based scenario generation and reduction approach. A novel and nature-inspired Black Widow Optimization (BWO) is applied to determine the optimal scheduling configuration. The effectiveness of the BWO is validated in terms of rate of convergence, computational time, and solution efficacy. Finally, the best DRP has been chosen based on its technical and economic performance indices by employing a multi-criteria decision-making approach.

Two-Cored Energy Management System for Industrial Microgrid

Energy systems have to deal with energy cost and environmental concerns such as greenhouse gas emission. Industrial buildings considered as Microgrid (μG) with heavy load worsen these issues even more. Further, cyber-attacks on the data communication channel between utility and customer is also a potential threat and may alter the data as well as the confidentiality of it, resulting in an inaccurate result. To address these problems, this paper proposes two-cored Building Energy Management System (BEMS) for Industrial Microgrid (IμG) with first cored termed as the energy layer concentrating on energy cost and emission reduction, while second cored termed as security layer provides the un-authorized intrusion detection and prevention system (IDS/IPS) for cyber secure communication of data. The μG under consideration contains national grid, Natural Gas (NG), solar Photovoltaic (PV) as input carriers; electrical energy at output ports; electric vehicle (EV) fleet; battery bank; solar PV panel as non-dispatchable Distributed Energy Resources (DERs) and Internal Combustion Engine (ICE), Fuel Cell (FC) and Micro Turbine (MT) as dispatchable DERs. Energy layer optimization problem has been solved in MATLAB using flower pollination algorithm for µG energy consumption cost and emission reduction. To develop and analyze the security layer, Linux operating system based Smooth-sec software has been used. Devised security layer continuously monitors the network traffic between customers and BEMS as well as BEMS and utility server. During monitoring it distinguish the licensed user or malicious attacker to detect and prevent possible internal and/or external intrusions in the communication channel. Results show that EMS reduces energy cost and emission in addition to cyber security from internal threats. Proposed two-cored control may be manufactured for utilities to realize its benefits for industrial customers in a smart energy distribution system. Keywords: Demand Response, Energy Management System, Flower