Optimal Energy Management System Research Articles

An optimal home energy management system for modulating heat pumps and photovoltaic systems

Efficient residential sector coupling plays a key role in supporting the energy transition. In this study, we analyze the structural properties associated with the optimal control of a home energy management system and the effects of common technological configurations and objectives. We conduct this study by modeling a representative building with a modulating air-sourced heat pump, a photovoltaic(PV) system, a battery, and thermal storage systems for floor heating and hot-water supply. In addition, we allow grid feed-in by assuming fixed feed-in tariffs and consider user comfort. In our numerical analysis, we find that the battery, naturally, is the essential building block for improving self-sufficiency. However, in order to use the PV surplus efficiently grid feed-in is necessary. The commonly considered objective of maximizing self-consumption is not economically viable under the given tariff structure; however, close-to-optimal performance and significant reduction in solution times can be achieved by maximizing self-sufficiency. Based on optimal control and considering seasonal effects, the dominant order of PV distribution and the target states of charge of the storage systems can be derived. Using a rolling horizon approach, the solution time can be reduced to less than 1min (achieving a time resolution of 1h per year). By evaluating the value of information, we find that the common horizon of 24h for prediction and control results in unintended but avoidable end-of-horizon effects. Our input data and mixed-integer linear model developed using the Julia JuMP programming language are available in an open-source manner.

Optimal energy management system for distribution systems using simultaneous integration of PV-based DG and DSTATCOM units

The energy management system (EMS) of an electrical distribution system (EDS), with the integration of distributed generation (DG) and distribution static compensator (DSTATCOM), provides numerous benefits and significantly differs from the existing EDSs. This paper presents an optimal integration of DG based on photovoltaic (PV) solar panels and DSTATCOM in EDS. A single objective function, based on maximizing the active power loss level (APLL) in EDS, is deployed to find the optimal size and location of photovoltaic DG and DSTATCOM simultaneously in different study cases using various particle swarm optimization (PSO) algorithms. These PSO algorithms are the basic PSO, adaptive acceleration coefficients PSO (AAC-PSO), autonomous particles groups for PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). These algorithms are applied to the standard IEEE 33- and 69-bus EDSs, which are used as test systems to verify the effectiveness of the proposed algorithms. Simulation results prove that the TVA-PSO algorithm exhibits higher capability and efficiency in finding optimum solutions. Comparing the simulation results attained for different study cases leads to the conclusion that DG and DSTATCOM were optimally-allocated simultaneously, which resulted in a significant reduction of power losses and an enhancement of the voltage profile.

Optimal Slewing Mode Converter-based Energy Management System for Renewable Energy sources

Optimal Slewing Mode Converter-based Energy Management System for Renewable Energy sources

Day-Ahead Energy Management for Pelagic Island Microgrid Groups Considering Non-Integer-Hour Energy Transmission

Pelagic islanded microgrid groups (PIMGGs) can be developed into resource islands and load islands with the electric vessel achieving the interisland energy transmission. For such multi-microgrid structure, the interisland energy transmission time affected by environmental factors is likely to be non-integer hour (such as 23min), resulting in mismatching with the standard day-ahead scheduling step (1h). However, rounding the transit time may bring about large errors in optimization results, while using an elaborate scheduling step to approach the transit time will result in heavy computing burden due to numerous variables. To address the above problems, this paper proposes a novel day-ahead energy management system (EMS). Firstly, analyzing the impact of environmental factors including wind, ocean current, and wave, a centralized energy management framework considering the non-integer-hour energy transmission is designed. Furthermore, an optimal EMS operation model based on the 1-hour scheduling step is proposed. It eliminates the errors from rounding by coordinating the original and mirror unit commitments. Additionally, the optimization model is converted into a mixed-integer linearization programming (MILP) problem using a two-stage method including preprocessing and linearizing stage. Simulation studies on Taiping Island, Honma Island and Bolan Reef indicate the proposed EMS is effective and beneficial for PIMGGs.

Adaptive leader election for control of tactical microgrids

An adaptive leader election protocol (LEP) was developed to control both stationary and mobile generation assets (generators and vehicles), achieved using an energy management system (EMS). The LEP algorithm adapts to changes in both topology and the asset inventory using the longevity criterion (available fuel, future availability), used to compute a desirability index, for election of a leader. The leader then implemented an optimal power flow EMS to ensure sufficient and optimal power flow within the electrical network was maintained in the presence of a complex electrical load, regardless of the asset mix. Both the LEP and EMS algorithms were distributed to the generation assets. This capability supports stationary grid-tied, vehicle-to-grid, and mobile vehicle-to-vehicle-based applications. Simulated case studies illustrate that the adaptive LEP was resistive to deterministic events (maintenance, available fuel), which could yield an inoperable asset, compromising grid stability. The use of the adaptive LEP resulted in a communication complexity of at most [Formula: see text]; in contrast, a fully connected communication system requires [Formula: see text] communications, limiting the scalability of the network. The EMS was optimized, resulting in a computationally efficient and scalable optimal power flow algorithm that can be extended for more general stationary or mobile energy networks.

Energy Scheduling of Isolated Microgrid with Battery Degradation Cost using Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients

The implementation of renewable generators together with a battery storage into an isolated Microgrid (MG) has become essential to minimise fuel utilization and contribute to maintain continuous supply of electricity. This paper studies the optimal set point of isolated MG units containing renewable generators, diesel generators and battery storage. The optimal energy dispatch of MG’s units is determined to supply the required load demand for a 48h horizon time. As the battery device has an important contribution in the MG, this paper proposes to implement battery degradation cost in the optimization model in addition to the fuel cost function. For this purpose, the Rainflow algorithm is used to count charging-discharging cycles and quantify the battery degradation. In addition, a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) algorithm is used to solve the defined objective function for an optimal energy management system of the isolated MG. A Weight Factor (WF) is proposed in the objective function. For the simulation tests, different values of WF are considered. The impact of WF is analysed on the algorithm behaviour, on the status of the State Of Charge (SOC) of the battery and its influence on the optimized MG cost function. The results demonstrate that the selection of an appropriate value of WF allows to the H-PSO-SCAC algorithm to achieve the best solution. In addition, with WF equals to 0.5, the charge/discharge cycles are reduced and the battery SOC is more stable.

Optimal Energy Management System Design Based on Dynamic Programming for Battery Electric Vehicles

Abstract An optimal control strategy for the energy management system on battery electric vehicles with only one power source is proposed. Firstly, this paper builds the control-oriented models for the drivetrain, battery thermal management and battery aging. Then, it is stated that, taking drivetrain power loss, battery thermal management requirements, and battery health performance into consideration simultaneously would lead to a multi-objective optimal control problem. Dynamic programming is adopted to solve the optimization problem. The simulation results could testify the feasibility and efficiency of the proposed energy management strategy in this paper.

A new isolated renewable based multi microgrid optimal energy management system considering uncertainty and demand response

This paper proposes a novel energy management system (EMS) for an isolated structure of networked microgrids (NMGs). The interconnected microgrids consist of the cyber-physical connections for information and power exchanges. A bi-level EMS is presented in which the outer-level EMS is aimed to exchange the required information and power between the interconnected microgrids, and the inner-level EMS is intended for energy scheduling of each on-fault microgrid in case of separation from other microgrids. This paper focuses on the operation of interconnected microgrids. A step-wise demand response program (DRP) is also considered in the energy management to attain the cost-effective operation. Furthermore, a new pricing model based on microgrid marginal pricing (MGMP) is introduced for the power exchanges between the interconnected microgrids. To cope with the uncertainties of the renewable energy sources and loads, some scenarios are generated using the scenario-based analysis. Also, a backward scenario reduction method is used to reduce the number of the scenarios. Besides, a mixed integer linear programming (MILP) is applied to the stochastic optimization problem of the NMGs. The proposed model is implemented on a test system with five NMGs. The simulation is run over a 24-hour scheduling time horizon. Both cases without and with demand response program (DRP) are compared in the numerical results. The results of the simulation demonstrate that using the proposed DRP in the energy management increases the performance of the generation units and decreases the total operational cost of the proposed NMGs. Also, the voltages of the buses converge to their rated values.

A Cost-Efficient Energy Management System for Battery Swapping Station

Extensive penetration of electrified transportation (xEVs) would require a framework pertaining to smart grid applications for optimizing the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) management. Under this framework, an xEV aggregator would be an intermediary between an independent system operator and individual xEVs, which would offer flexibilities for operating electricity markets. In this paper, we propose a mix integer linear programming based optimal energy management system for battery swapping station (BSS) by participating in the day ahead, real-time and ancillary services. To incorporate the dynamicity of energy price and xEVs load in the optimization model, a suitable forecasting method is selected and suitability of the selected model has been discussed. Furthermore, we propose an incentive-based vehicle-to-vehicle game theoretic approach to assess the expected profit of the BSS aggregator. Later, the validation of the proposed framework is discussed through a case study considering a fleet of 100 xEVs. To incorporate real-time scenario, multitype electric vehicles (EVs) are considered.

Optimal Energy Management of Railroad Electrical Systems with Renewable Energy and Energy Storage Systems

The proposed optimal energy management system balances the energy flows among the energy consumption by accelerating trains, energy production from decelerating trains, energy from wind and solar photovoltaic (PV) energy systems, energy storage systems, and the energy exchange with a traditional electrical grid. In this paper, an AC optimal power flow (AC-OPF) problem is formulated by optimizing the total cost of operation of a railroad electrical system. The railroad system considered in this paper is composed of renewable energy resources such as wind and solar PV systems, regenerative braking capabilities, and hybrid energy storage systems. The hybrid energy storage systems include storage batteries and supercapacitors. The uncertainties associated with wind and solar PV powers are handled using probability distribution functions. The proposed optimization problem is solved using the differential evolution algorithm (DEA). The simulation results show the suitability and effectiveness of proposed approach.

A State of the Art Review of Electric Vehicle to Grid (V2G) technology

Greenhouse gas emission, fast depletion of fossil fuels, the oil crisis and the increased cost of petroleum products are the major factors that need a shift from internal combustion engines to Electric Vehicles. The commercial deployment of EVs requires a large charging infrastructure to charge and discharge the EVs. Vehicle to Grid is a new emerging technology which came into existence because a large number of EVs can be used as load as well as an energy storage system to support the grid. However, the uncoordinated EV charging shows the crucial impact on the power system. Thus, optimum coordination of the V2G system needed. Thus, this paper presents a comprehensive state of art research of V2G system. The presented paper describes the methodology adopted for power flow under V2G scheme. Further, it explains the key barriers for adoption of V2G at a commercial level. Moreover, the state of art comparison is given for V2G, vehicle to home (V2H) and vehicle to vehicle (V2V). Later, the various optimization techniques have explained how they support the optimal energy management system under a coordinated V2G system.

Optimal Real-Time Residential Thermal Energy Management for Peak-Load Shifting With Experimental Verification

Controlling residential thermal loads and thermal energy storage is a viable tactic to engage end-users in demand response programs (DRPs). This paper focuses on the development of an optimal real-time thermal energy management system (TEMS) for smart homes to respond to DRP for peak-load shifting. The proposed TEMS combines two model predictive controllers to manage two thermal energy storage systems, a water storage tank (WST) and the building thermal mass, to schedule residential heat pump loads to off-peak periods. The intention is to manage the operation of a ground source heat pump (GSHP) to produce the desired amount of thermal energy by controlling the volume and temperature of the stored water in the WST while optimizing the operation of the heat distributors to control indoor temperature. The primary contributions are the development of a new control strategy for GSHPs coupled with WST based on building identification to minimize total energy consumption and cost. This paper also proposes a real-time indoor dynamic temperature set-point strategy based on real-time pricing tariffs for enhancing peak-load shifting of heat pump loads with an acceptable variation in thermal comfort. Simulation and experimental results demonstrate that the proposed TEMS has significant potential for real-time peak-load shifting.

Practical Energy Management Systems for Isolated Microgrids

This paper presents practical energy management system (EMS) models which consider the operational constraints of distributed energy resources, active-reactive power balance, unbalanced system configuration and loading, and voltage dependent loads. A novel linearization approach is proposed and validated based on the fact that, for isolated microgrids, due to the characteristics of feeders, network losses, and voltage drops across feeders are relatively small. The proposed EMS models are mixed integer quadratic programming problems, requiring less computation time and thus suitable for online applications. The practical EMS models are compared with a typical decoupled unit commitment and optimal power flow-based EMS with and without consideration of system unbalancing. The models, along with “standard” EMS models, are tested, validated, and compared using a CIGRE medium voltage benchmark system and the real isolated microgrid of Kasabonika Lake First Nation in Northern Ontario, Canada. The presented results demonstrate the effectiveness and practicability of the proposed models.

Critical Success Factors Evaluation of the ISO 50001 Energy Management System Implementation (Case study: PT. APAC INTI CORPORA, Bawen, Semarang Indonesia)

Rapid growth makes the industrial sectors contribute as a significant greenhouse gasses producer. According to the Indonesian Ministry of Industry data, the industrial sectors dominate energy consumption with a share of 49.4% of total national energy consumption. These energy needs tend to increase along with economic growth and global population rise. Based on this problem, development of standards related to energy management system is needed for the industrial sector. PT. APAC INTI CORPORA (AIC) is a textile company, located in Bawen, Semarang Indonesia. PT. AIC has implemented ISO 50001 since 2014 as an energy management system to improve the company’s service quality and financial benefit. In this study, Critical Success Factors from the application of ISO 50001 at PT. AIC will be evaluated to assist companies in implementing a more optimal energy management system. A critical success factor is an area in the form of indicators that identify the success of an organization’s performance. Data processing resulted in 8 improvement strategy points based on importance weights (AHP Method) and performance values (Performance Rating) for each subfactor. Through the Delphi method, the improvement strategies is adjusted to the company’s conditions and reduced to 6 strategy points.

Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts

Abstract In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a network of electrified Rubber Tyre Gantry (RTG) cranes. The stochastic management system aims to improve the reliability and economic performance, for given ESS parameters, of a network of cranes by taking into account the uncertainty in the RTGs electrical demand. A specific case study is presented using real operational data of the RTGs netwrok in the Port of Felixstowe, UK, and the results of the stochastic control system is compared to a standard set-point controller. In this paper, two forecast data sets with different levels of accuracy are used to investigate the impact of the crane demand forecast error in the proposed ESS control system. The results of the proposed control strategies indicate that the stochastic management system successfully increases the electric energy cost savings, the peak demand reductions and successfully outperforms a comparable set-point controller.

Hybrid optimal management of active and reactive power flow in a smart microgrid with photovoltaic generation

This paper presents a new approach to the optimal power flow management for low-voltage urban microgrid (UMG) connected to the power grid (PG). The considered UMG consists of a photovoltaic generator, an electrochemical storage system, a micro-gas turbine (GT) and a residential loads. A new optimal energy management system (EMS) is designed to perform a day-ahead power flow management (off-line management) in the UMG. The proposed EMS aims at coordinating the active and the reactive power schedule for the distributed energy sources (DES). This EMS is based on 24 h ahead forecast data of the residential loads consumption, the photovoltaic power and the electricity tariffs. Indeed, the EMS predicts hour-by-hour the active/reactive power references for each DES, while ensuring the stability and the reliability of the UMG. Accordingly, the EMS has two objectives: (1) minimizing the production cost of the active/reactive power for each DES (economic criteria); (2) reducing the CO2 equivalent emissions by adjusting the GT operating point (environmental criteria). To meet these objectives, the optimization problem is stated as a discretized economic problem over a 24 h time horizon. This problem is formulated by a multi-objective function (expressed in cash flow) as well as the operating constraints of the DES. To solve the optimization problem, a hybrid optimization technique is adopted based on the dynamic programming (using the Bellman algorithm) combined with the linear programming (using the simplex algorithm). This resolution choice allows an exhaustive and effective exploration of the solutions search area in a relatively fast resolution time (CPU time). The effectiveness of the proposed management strategy is demonstrated using a comparison against a rules-based management. Indeed, a statistical comparison shows that the proposed management provides an optimization around 38% of the energy bill with respect to the rules-based one.

A model for integrating energy management in lean production

The implementation of sustainable production systems has been the focus of political, economic and social efforts for years. Due to the switch from fossil fuels to green energies, energy costs have been rising steadily for years. Energy-related taxes and levies can be reduced with a certified energy or environmental management system.ISO 50001 specifies requirements for the implementation of energy management system. Nevertheless, since 2017, ISO 50003 requires companies to demonstrate, by means of indicators, that energy efficiency is actually being continuously improved. Until now, only the proof, which shows that a systematic approach to reduce energy consumption is employed, was sufficient as a basis. However, the focus has now shifted towards the optimization of energy management systems. Nonetheless, these norms allow a wide scope for interpretation.This paper contains a methodology for the implementation of energy management system according to which companies can design there structure and processes to fulfil all the requirements specified in ISO 50001 and the other relevant energy management standards. The technical, business and organizational possibilities as well as the involvement of personnel along the McKinsey 7S model are considered. McKinsey’s 7S change management tool provides the framework. The sustainable success of companies is analyzed using hard and soft factors. In this case, the model is applied to management systems.

Frequency Separation Model Based on Infinite-Impulse Response Filter Applied To Hybrid Power Generation Intended For Residential Sector

This paper presents an optimal energy management system by means of frequency separation control, applied to an autonomous hybrid power generation intended for residential sector. Studied system is integrating photovoltaic and wind energy systems as main sources, and fuel cell stack as an auxiliary source. This residential hybrid power system is also including battery to support fast and large power demand fluctuation in order to preserve the fuel cell reliability and to enhance its lifetime. The models of the photovoltaic, wind, battery, fuel cell systems and the control strategy are carried out under MATLAB/SIMULINK. To display the performance of the studied energy management system, many scenarios are designed, in which the wind speed, the irradiance intensity and the load demand change over time. Simulation results illustrate the efficiency of the considered energy management system. The proposed strategy is validated experimentally in a lab-scale prototype. Experimental results are then presented to confirm the whole system performance and to verify the proposed controller efficiency.

A two-stage optimal energy management by using ADP and HBB-BC algorithms for microgrids with renewable energy sources and storages

Abstract Energy management system (EMS) plays a key role to control and maintain efficiency and reliability of micro-grids (MGs) at satisfactory levels. In this paper, a two-stage optimal EMS is designed for MGs with renewable energy sources and storages, and residential and industrial loads, with the capability to predict stochastic electrical demands and electricity prices with respect to operational and model uncertainties. A two-stage optimization method, based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm, is developed to optimally manage power generation, distribution and saving within MGs. Conditions of the main grid, operational cost functions are fully addressed into the design. In particular, Time of Use (ToU) tariffs and curtailable loads are considered to implement a Demand Response (DR) program to efficiently enhance the optimization performance. The proposed method is applied to a residential MG benchmark system, and simulation results are discussed and compared in various scenarios.

A daytime optimal stochastic energy management for EV commercial parking lots by using approximate dynamic programming and hybrid big bang big crunch algorithm

The design of optimal energy management systems has been an important problem, in particular for daytime plug-in electric vehicles’ (PEVs’) parking lots at workplaces and commercial buildings, where the number of vehicles, their arrival and departure times are typically unknown and time-varying. The paper addresses this problem by introducing a two-stage optimization based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm considering a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) which predicts the electricity price. The proposed optimal energy management minimizes the cost of parking lot owner with respect to the Time of Use (TOU) Demand Response (DR) program without reducing the welfare of EV owners. The two-stage optimization manages the charge scheduling subject to short and long term data coming from the MLP-ANN and intelligent transportation system. The stochastic features of the commercial parking are fully addressed into the problem. The effectiveness of the proposed charging technology is assessed and discussed by using real electricity data from ERCOT, under various stochastic conditions. The results demonstrate optimal energy management during peak and off time periods at the minimum cost.