Energy Management System Algorithm Research Articles

Optimal EMS Design for a 4-MW-Class Hydrogen Tugboat: A Comparative Analysis Using DP-Based Performance Evaluation

In the current trend of hydrogen fuel cell-powered ships, batteries are used together with fuel cells to overcome the limitations of fuel cell technology. However, performance differences arise depending on fuel cell and battery configurations, load profiles, and energy management system (EMS) algorithms. We designed four hybrid controllers to optimize EMS algorithms for achieving maximum performance based on target profiles and hardware. The selected EMS is based on a State Machine, an Equivalent Consumption Minimization Strategy (ECMS), Economic Model Predictive Control (EMPC), and Dynamic Programming (DP). We used DP to evaluate the optimal design state and fuel efficiency of each controller. To evaluate controller performance, we obtained a 4-MW-class tug load profile as a reference and performed simulations based on Nedstack’s fuel cells and a lithium-ion battery model. The constraints were set according to the description of each equipment manual, and the optimal controller was derived based on the amount of hydrogen consumed by each EMS under the condition of completely tracking the load profile. As a result of simulating the hybrid fuel cell–battery system by applying the load profile of the tugboat, we found that the 4-MW EMPC, which requires more state variables and control inputs, is the most fuel-efficient controller.

Robust Energy Management System With Safe Reinforcement Learning Using Short-Horizon Forecasts

In this letter, we study an energy management system (EMS) with an inconsistent energy supply that aims to minimize energy costs while avoiding failing to satisfy energy demands. To this end, we propose a robust EMS algorithm based on safe reinforcement learning which can effectively exploit short-horizon forecasts on system uncertainties. We show via experimental results using real datasets that our robust EMS algorithm outperforms other state-of-the-art algorithms in terms of both robustness and cost-efficiency thanks to its capability of utilizing short-horizon forecasts.

Energy management of photovoltaic-battery system connected with the grid

Managing a sustainable hybrid system may be accomplished in a variety of ways, including sizing, obtaining maximum power, or balancing multiple energy sources. The rapid growth and attractiveness of renewable energy technology require the development of an Energy Management System (EMS) architecture that integrates a PV-battery system into the grid. In the present study, a grid-connected hybrid power system to manage energy production, grid interaction, and energy storage is installed and experimentally investigated. The PV-battery system is connected to the grid and employs an optimal EMS algorithm, which has been validated using both virtual simulation and lab experiments to ensure serving the demand load while minimizing grid consumption. Simulation results show that the grid consumption over an entire year is minimized to represent 59.5 % of the whole consumption. According to simulation and laboratory results, the proposed EMS algorithm saves at least 40 % of the grid's energy use with the intended PV-battery system, while also aiding in the management of solar and grid instability by guaranteeing that electricity is accessible at all times.

Optimal economic dispatch of hybrid microgrids integrating Energy Storage Systems with Grid-Forming Converters

New control strategies based on Grid-Forming converters make possible the operation of isolated microgrids without the support of synchronous generation. This paper proposes an Energy Management System algorithm that optimizes the operation of a microgrid, maximizing the integration of renewable energy and considering the possibility of disconnecting all synchronous generators. The algorithm is applied to a microgrid modelled with real data and two economic dispatch strategies are analysed: the first requires keeping at least one synchronous generator connected to provide frequency regulation following standard practice; and the second considers the disconnection of all conventional generation relying on the capabilities of Grid-Forming converters when sufficient reserve is available. The results show that the proposed strategy reduces the operational cost of the system, as well as solar PV curtailment and diesel consumption.

Computation of electrical vehicle charging station (evcs) with coordinate computation based on meta-heuristics optimization model with effective management strategy for optimal charging and energy saving

The wide deployment of grid-connected environments leads to the growth of electric vehicles (EV). On other hand, it put forth the development of EV stations and reduced energy as well as arbitrage. The users concentrated on the charging point of the Electrical Vehicle Charging Stations (EVCS) to be minimal power variation at PCC (point of common coupling). Consequently, it demands improved energy storage for optimal placement and energy savings. This paper proposed a MOO (multi-objective optimization) method for reducing energy consumption and time. To achieve the objective optimal location is considered an effective tool for EVCS with coordinated charging. The proposed model incorporates the integration of MATPOWER integrates with a meta-heuristics algorithm for EMS (Energy Management System) in a microgrid. Placement is achieved with the objective of optimal energy saving by means of clustering in EVCS at various nodes of a microgrid at distributed energy sources.

New algorithm for energy dispatch scheduling of grid-connected solar photovoltaic system with battery storage system

Purpose. In last decade the problem of energy management system (EMS) for electric network has received special attention from academic researchers and electricity companies. In this paper, a new algorithm for EMS of a photovoltaic (PV) grid connected system, combined to an storage system is proposed for reducing the character of intermittence of PVs power which infect the stability of electric grid. In simulation model, the PV system and the energy storage system are connected to the same DC bus, whereas EMS controls the power flow from the PV generator to the grid based on the predetermined level of PV power. In the case where the PV power is less than the predefined threshold, energy is stored in the batteries banc which will be employed in the peak energy demand (PED) times. Otherwise, it continues to feed the principal grid. The novelty of the proposed work lies in a new algorithm (smart algorithm) able to determine the most suitable (optimal) hours to switching between battery, Solar PVs, and principal grid based on historical consumption data and also determine the optimal amount of storage energy that be injected during the peak demand. Methods. The solution of the problem was implemented in the Matlab R2010a Platform and the simulation conducted on Laptop with a 2.5 GHz processor and 4 GB RAM. Results. Simulation results show that the proposed model schedules the time ON/OFF of the switch in the most optimal way, resulting in absolute control of power electric path, i.e. precise adaptation at the peak without compromising consumers comfort. In addition, other useful results can be directly obtained from the developed scheme. Thus, the results confirm the superiority of the proposed strategy compared to other improved techniques.

Toward the Optimal Operation of Hybrid Renewable Energy Resources in Microgrids

Renewable energy sources are environmentally friendly and cost-efficient. However, the problem with these renewable resources is their heavy reliance on weather conditions. Thus, at times, these solutions are not guaranteed to meet the required demand all the time. For this, hybrid microgrids are introduced, which have a combination of both renewable energy sources and non-renewable energy resources. In this paper, a cost-efficient optimization algorithm is proposed that minimizes the use of non-renewable energy sources. It maximizes the use of renewable energy resources by meeting the demand for utility grids. Real data based on the load and demand of the utility grids in Italy is used, and a system that determines the optimal sizing of the microgrid and a daily plan is introduced to optimize the renewable resources operations. As part of the proposal, the objective function for the operation and planning of the microgrid in such a way to minimize cost is formulated. Moreover, a variant of the PSO algorithm named recurrent PSO is implemented. The recurrent PSO algorithm solves the proposed optimization objective function by minimizing the cost for the installation and working of the microgrid. Afterwards, the energy management system algorithm lays out a plan for the daily operation of the microgrid. The performance of the system is evaluated using different state-of-the-art optimization methods. The proposed work can help minimize the use of diesel generators, which not only saves financial resources but also contributes toward a green environment.

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.

An Affine Arithmetic-Based Energy Management System for Isolated Microgrids

This paper presents a mathematical formulation of an energy management system (EMS) for isolated microgrids, which addresses uncertainty using the affine arithmetic (AA) method. The proposed EMS algorithm is based on an AA unit commitment (AAUC) problem for day-ahead dispatch, using uncertainty intervals of both load and renewable energy (RE) to provide robust commitment and dispatch solutions in AA form, which are feasible for all the possible realizations within the predetermined uncertainty bounds. A real-time dispatch solution is then found by the proposed algorithm, which computes the noise symbols values of the affine forms obtained by the AAUC, based on the current and actual load and RE power levels and available reserves. If the actual forecast error is outside the uncertainly bounds considered in the AAUC solution process, leading to possible load and/or RE curtailment, the AAUC is recalculated with updated forecast information. The proposed AA-based EMS is tested on a modified CIGRE microgrid benchmark and is compared against day-ahead deterministic, model predictive control (MPC), stochastic optimization, and stochastic-MPC approaches. The simulation results show that the proposed EMS provides robust and adequate cost-effective solutions, without the need of frequent re-calculations as with MPC-based approaches, or assumptions regarding statistical characteristics of the uncertainties as in the case of stochastic optimization.

Approaches to Economic Energy Management in Diesel–Electric Marine Vessels

Recently, the efficiency of diesel–electric marine vessels has been subject for discussion with focus on improving fuel efficiency, reducing the environmental footprint from emissions, as well as reducing running hours and maintenance costs. This paper presents an analysis of load profiles extracted from three different vessels during operation: 1) a ferry; 2) a platform supply vessel; and 3) a seismic survey vessel. The analysis of the extracted data shows that the loadings of the diesel engines are typically quite low, and do not fall within the optimal loading range of diesel engines’ specific fuel oil consumption curves. Furthermore, three different power plant configurations are proposed and compared, which include fixed-speed (diesel engine generators) and variable-speed gensets and the implementation of an energy storage system. Moreover, energy management system (EMS) algorithms based on mixed-integer linear programming (MILP) are proposed as a suitable strategy for optimal unit commitment in the power generation. The results yielded from the MILP algorithms are compared with those from the EMS algorithms based on logic such as if/else statements. The results indicate that optimal EMS algorithms in combination with a revised vessel configuration can increase the operational efficiency, in terms of fuel savings and reduction in genset running hours.

Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization

In this paper, an algorithm for energy management system (EMS) based on multi-layer ant colony optimization (EMS-MACO) is presented to find energy scheduling in Microgrid (MG). The aim of study is to figure out the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real time scheduling. The proposed algorithm is based on ant colony optimization (ACO) method and is able to analyze the technical and economic time dependent constraints. This algorithm attempts to meet the required load demand with minimum energy cost in a local energy market (LEM) structure. Performance of MACO is compared with modified conventional EMS (MCEMS) and particle swarm optimization (PSO) based EMS. Analysis of obtained results demonstrates that the system performance is improved also the energy cost is reduced about 20% and 5% by applying MACO in comparison with MCEMS and PSO, respectively. Furthermore, the plug and play capability in real time applications is investigated by using different scenarios and the system adequate performance is validated experimentally too.

Optimal energy management for stand-alone microgrids based on multi-period imperialist competition algorithm considering uncertainties: experimental validation

Summary Microgrid (MG) constitutes non-dispatchable resources and responsive loads, which can serve as a basic tool to reach desired objectives while distributing electricity more effectively, economically, and securely. However, high penetration of distributed generations into the grid leads to fundamental and critical challenges to ensure a reliable power system operation. This paper presents a general formulation of optimum operation strategy with the objective of cost optimization plan and demand response regulation. MG energy management problem can be formulated as an optimization problem in order to minimize the cost-related to generation resources and responsive loads. An expert heuristic approach based on multi-period imperialist competition algorithm is applied to implement an energy management system for optimization purposes. A comparison is carried out between the proposed algorithm and classical techniques, including particle swarm optimization and a modified conventional energy management system algorithms. An artificial neural network combined with Markov-chain approach is used to predict non-dispatchable power generation and load demand under uncertainty conditions. The proposed algorithm is evaluated experimentally on an MG testbed, and the obtained results demonstrate the efficiency of the proposed algorithm to minimize the total generation cost with a fast calculation time, which makes it useful for real-time applications. Copyright © 2015 John Wiley & Sons, Ltd.

Impact on electricity use of introducing time-of-use pricing to a multi-user home energy management system

Summary Home energy management system technology can provide a smart and efficient way of optimizing energy usage in residential buildings. This paper presents a home energy management system algorithm that monitors and controls household appliances based on time-of-use (TOU) energy pricing models while accounting for multiple inhabitants sharing a home and its appliances. This algorithm helps to manage and schedule usage by prioritizing multiple users with preferred usage patterns. Two different scenarios will be implemented to develop and test the influence of a multiple-users and load priority (MULP) algorithm on reducing energy consumption, energy cost and carbon footprint. In the first scenario, TOU pricing and different demand limits are used, while the second scenario focuses on the TOU pricing with different demand limits combined with the MULP model. Simulation results show that the combination of the MULP model and the TOU pricing leads to significant reductions in user payments and total energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.