Effective Energy Management System Research Articles

Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

Real-time Energy Management for PV–battery–wind based microgrid using on-line sequential Kernel Based Robust Random Vector Functional Link Network

This paper comprises of an effective Local Energy Management System (LEMS) implementing the Generalized Power Prediction Model (GPPM) for the uncertain performance of various Distributed Generations of a microgrid. The primary DGs considered to compensate intermittency is Wind Power Generation System (WPGS) and Photovoltaic (PV) along with Battery Energy Storage (BES). Solar and wind power are considered here as prediction targets and to cope with the intermittent nature of the Renewable Energy Sources (RESs), Power calculation from the RES data is included in the proposed prediction model. In order to establish arobust reduction in the predicted error, areal-time Online Sequential Kernel based Robust Random Vector Functional Link Network (OS-KRRVFLN) prediction algorithm is developed. Here, the initial randomness of the proposed OS-KRRVFLN is reduced (robust performance) by Hampel’s cost function (eP based) minimization. The PV-BES and WPGS based DGs are designed to be operated under certain uncertainties like PV arc, bus faults etc. as uninterrupted power supply with minimum grid power dependency. An efficient Distributed Adaptive Droop (DAD) control is thus opted as Primary Controller (PC). Addictiveness of DAD’s operational region is estimated while compensating eP”-. The coordination of GPPM with DAD-PC based accurate reference estimation for Independent DG Controllers (IDGCs) operation is considered as LEMS operation.

Energy Management Practices in State Universities and Colleges (SUCs): An Assessment for an IOT Intervention

Monitoring and analyzing a building’s energy consumption patterns are major components for an effective energy management system, which helps in understanding the building’s operational behavior under different conditions. Energy management teams of universities are responsible for monitoring, analyzing and maintaining energy consumption data of their buildings. The study was conducted to elucidate the status of energy consumption, and to identify and illustrate the potential weak areas found from the energy consumption gap analysis in the context of government HEIs. The gap in this study was addressed by providing a detailed assessment of energy consumption practices and insights in an intra-university context, primarily through the identification of key practices leading to energy-saving measures. The work presented in this paper relied on data obtained from energy audits from the complete set of buildings over several campuses. In the occurrence of disparity between the actual values and the theoretical values, a focused group discussion was conducted to determine the energy consumption practices. The researchers had identified and categorized lists of practices based on the literature and selected those most applicable for government-funded HEIs. The researchers identified the list of key practices anchoring three Energy Management models: ISO 5001, Energy Star and BRECSU Energy Management System. The study determined that addressing specific key practices leading to an effective energy management system helps develop clear and achievable goals and objectives that adopt the SMART concept in the implementation of energy management programs.

Double-layer energy management system based on energy sharing cloud for virtual residential microgrid

The idea of energy sharing can contribute to achieving the goal of resource optimization by redistributing and sharing idle energy assets. How to design an appropriate energy management strategy in the energy sharing environment has been the focus of intensive research in energy sharing field. In this paper, a new effective double-layer energy management system (EMS) based on the energy sharing cloud (ESC) is developed for a virtual residential microgrid (VRMG). The proposed ESC can be regarded as an open energy sharing environment, where the cloud platform helps cloud users build their VRMGs by providing energy services including renewable energy sources (RESs) generation and energy storage. The mathematical model of the VRMG is formulated, and the energy service prices for RESs generation and energy storage are monthly set separately. Moreover, considering the changes in household load and RESs generation, an energy management strategy of double-layer EMS is designed. The upper-layer EMS helps VRMG obtain the monthly optimal capacity configuration of RESs and energy storage, and the lower-layer EMS realizes the daily electricity scheduling optimization for the VRMG whose objectives are to minimize the total operational cost and maximize the electrical comfort level. Simulation studies demonstrate that the proposed energy sharing mechanism can meet the changing energy needs of the cloud user, and numerical experiments also confirm the performance and effectiveness of the proposed double-layer EMS.

A Comparative Analysis of Data-Driven Based Optimization Models for Energy-Efficient Buildings

Energy-efficient and sustainable buildings have become a specialized research theme for energy planning and the green building industry. Rapidly increased energy consumption in the residential area has attracted researchers' attention to minimize energy consumption without affecting occupant comfort. Accordingly, an effective energy management system is required for energy-efficient buildings to utilize energy resources efficiently and maintain desired comfort. In this paper, data-driven based optimization models are compared to maximize the comfort index and minimize energy consumption simultaneously. Heating-cooling systems are used to maintain indoor thermal comfort, which consumes energy according to the environmental temperature and indoor temperature difference. Therefore, the environmental temperature parameter is optimized using optimization techniques Genetic Algorithm (GA), Bat, Neural Network Algorithm (NNA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The main objective of the optimization is to reduce the gap/error between the temperature specified by the user and the environmental temperature. Afterward, the discrepancy between optimized temperature and actual temperature is fed to the designed ML (Machine Learning) based controller. The controller's output is further provided to coordinator agents, which give the power to the actuators accordingly. The linearity of the proposed model improves the performance of the ML algorithm. The obtained dataset from fuzzy temperature controller has been used to design an ML controller. The experimental results show that the designed system significantly improves energy efficiency and occupant comfort for energy-efficient buildings. The BAT model has shown effectiveness in achieving a high comfort index with minimum power consumption compared to other considered models.

Energy Management System for DC Electric Spring With Parallel Topology

DC electric spring (DCES) maintains the voltage stability and improves the power quality in dc microgrid with the inclusion of renewable energy sources. A parallel DCES topology has been recently proposed; it consists of a dc–dc three-port converter and an energy storage system made up of a battery paralleled with the critical load (CL) through a bidirectional buck–boost converter. In this article, an effective energy management system (EMS) is proposed for the parallel DCES topology. After illustrating the DCES operating modes, three functional states are introduced, namely normal, specific time, and emergency, where the normal state refers to the DCES operation under dc microgrid power fluctuations, whereas the specific time and emergency states refer, respectively, to the environment conditions and battery contingent situations. The EMS decides the transitions between the DCES operating modes, changes the operating status of the battery, and realizes multiple charging and discharging mechanism. This gives DCES the capabilities of guaranteeing the voltage stability of CL while keeping the state of charge of the battery within a reasonable range, which extends the lifetime of battery and ensures safe and reliable operation of the whole system. The proposed EMS has been validated by simulation and experimental results.

Simulation of a Hybrid Energy System for Stratospheric Airships

Effective energy management systems are critical for long endurance stratospheric airships. This article presents a hybrid energy system consisting of one renewable (photovoltaic) and two energy storage (lithium battery/regenerative fuel cell) systems. After examining different models of energy harvesting, balancing, and storage, we present the details of a rule-based energy management strategy. We further validate it by presenting the various power curves and state of charge (SOC) measurements. The results indicate that the strategy is effective for energy management planning.

A Rechargeable Zinc-Copper Battery Based on the Combination of a Monovalent Cationic Exchange Membrane and an Inert Charge Shuttle

To reach their full potential, renewable and intermittent energy sources such as wind and sun need to be coupled with a cost effective energy management system that is able to convert, store, and delivery energy as required. The Daniell element is one of the earliest non-rechargeable batteries. It is based on abundant, cost-effective and environmentally-benign materials and the chemistry relies on reduction of copper at the positive electrode and oxidation of zinc at the negative electrode. Since both electrodes are immersed in different electrolytes, they have to be separated by a porous barrier or a salt-bridge in order to prevent the self-discharge of the battery. However, these separators are not selective and copper ions migrate over time to the negative electrode and deposit on the surface of the zinc electrode which reduces shelf time and makes recharging of the element impossible.In this work, we report on a novel Zinc-Copper battery design which is based on the application of a selective cation exchange membrane in conjunction with an unreactive sodium background electrolyte which acts as a charge shuttle. The membrane is impermeable for the copper (and zinc) ions but allows the sodium ions to migrate between the two half-cells to maintain the electro-neutrality.We first tested the suitability of different zinc-based electrolytes, including zinc sulfate, zinc chloride, and zinc nitrate. At equal electrolyte conductivities, we found a higher potential for the zinc deposition in sulfate electrolytes compared to that in chloride electrolytes. It was also observed that the zinc electrode passivated due to the formation of zinc oxide (ZnO) when operated in a zinc nitrate solution. In terms of the electrodeposition of copper, sulfate and nitrate-based electrolytes showed a nearly equal potential. However, chloride-based copper electrolytes shifted the potential to more negative values due to the stabilization of the monovalent copper ion intermediate.The membrane selectivity was evaluated by monitoring the crossover of copper ions into the zinc half-cell for various concentrations of copper in the copper half-cell. After seven days of open circuit operation, we measured copper concentrations below 10 ppm in the zinc half-cell when there was 0.1 M of copper sulfate in the copper half-cell. When the copper half-cell was filled with 0.5 M copper sulfate, the effect of crossover was evident by a blue tint of the electrolyte and the Zn electrode was covered with a layer of black/red deposit.Hence, we chose a conservative catholyte composition of 0.1 M copper sulfate and 1M sodium sulfate in the battery. We performed discharge measurements where we varied the discharge current density and measured the voltage over time. Discharge continued until a cell voltage of 0 V was reached, which corresponds to a complete consumption of the copper ions in the catholyte. Indeed, it turned out that we utilized only around 3 to 6 % of the zinc electrode and less than 1 % of the copper electrode during discharge. These very low values indicate that the present system could also be favorably-designed as a hybrid redox (flow) battery. Hence, for the current preliminary cell design it is seems reasonable to normalize the current with the mass of the initial copper ion content since it is the limiting electro-active species. At 0.5 mA/cm2 (421 mA/g), the battery was discharged for around 17 hours before all of the copper ions were consumed resulting in a specific capacity of 763 mAh/g. The theoretical charge content based on the initial copper concentration was 843 mAh/g giving a faradaic yield of 90.5 %. The capacity and faradaic yield of the discharge at 1 mA/cm−2 (842 mA/g) were 583 mAh/g and 69.2 %, respectively.The recharge-ability of the battery was investigated over 100 charge-discharge cycles at a current density of ±0.5 mA/cm2. The average capacity was 530 mAh/g while the average exergy efficiency was 74 %. The deviations over the cycle numbers are less than 5 % of the average values. In other words, there was no clear increase or decrease during operation, indicating a steady performance. Afterwards, the zinc electrode was characterized using Energy-dispersive X-ray spectroscopy. Only a very small amount of deposited copper was found, indicating a negligible copper ion cross over. This verifies that the combination of a cost-effective monovalent cation exchange membrane along with an unreactive background electrolyte imparts recharge-ability to the Daniell cell. Figure 1

Optimal Scheduling of Grid Transactive Home Demand Responsive Appliances Using Polar Bear Optimization Algorithm

The conventional power system has been evolving towards a smart grid system for the past few decades. An integral step in successful realization of smart grid is to deploy renewable energy resources, particularly rooftop photovoltaic systems, at smart homes. With demand response opportunities in smart grid, residential customers can manage the utilization of their demand responsive appliances for getting economic benefits and incentives in return. In this regard, this paper proposes an effective home energy management system for residential customer to optimally schedule the demand responsive appliance in the presence of local photovoltaic and energy storage systems. For efficient home-to-grid energy transactions in home energy management system, the stochastic nature of photovoltaic power generation is modeled with the beta probability distribution function for solar irradiance. The main contribution of this paper is the application of polar bear optimization (PBO) method for optimally solving the scheduling problem of demand responsive appliances in home energy management system to minimize electricity consumption cost as well as peak-to-average ratio. The effectiveness of the proposed metaheuristic optimization technique is proven by performing different case studies for a residential consumer with different base load, uninterruptible deferrable, and interruptible deferrable appliances under a real-time energy price program. Comparative results with different metaheuristic techniques available in the literature show that the electricity consumption cost and peak-to-average ratio are effectively optimized using the proposed PBO algorithm.

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.

A Novel Smart Energy Management System in Pure Electric Motorcycle Using COA

Todays, Greenhouse Gases (GHG) emissions, densely populated urban areas air quality problems, and depleting fossil fuel concerns motivated motorcycle producers in crowded countries to explore electric propulsion systems. Due to energy storage system constraints, Pure Electric Motorcycles (PEM) range is restricted, so an effective Energy Management System is required to optimize PEM consumed energy. As road curvature is an inevitable part of real roads, this article, proposes a novel Smart Energy Management System (SEMS) based on road geometry which first analyses road data to identify curvature and straight parts. Then, SEMS solves optimization problems separately and optimal speed is calculated in direct and curvature parts independently. The proposed SEMS can communicate to the driver as an eco-driving system by receiving trip time importance and presenting each road point optimal speed. Because of fast response necessity, Cuckoo Optimization Algorithm (COA) is used in this article to solve SEMS optimization problem. For evaluation purposes, three scenarios are defined: The test road is travelled at motorcycle maximum allowable speed in the first scenario. In the 2nd one, PEM speed is also constant but it equals to direct road optimal speed instead of max. speed. Independent optimal speed in direct and curvature parts is considered in 3rd scenario. Simulation results show the significant effect of road curves on consumed energy and SEMS good performance in optimization of PEM consumed energy.

An Intelligent Energy Management Frame Work using Fuzzy Logic for Grid Connected Hybrid Energy System

India is one of the fastest growing countries in terms of energy consumption. Currently, it is the fifth largest consumer of energy in the world, and will be the third largest by 2030. The country is heavily dependent on fossil sources of energy for most of its demand. In response to present scenario of energy consumption, India is gradually shifting focus towards its renewable energy resources. Power can be delivered at maximum efficiency with maximum continuity through an effective energy management system. The energy management system should effectively and efficiently manage the power exchange among different units of systems and also reliably regulate the load to ensure that the load is always supplied. This paper proposes a Fuzzy Logic based optimal energy management Strategy for complex systems that comprises of PV Energy System, Wind Energy System, Fuel Cell and a Battery. The performance of the proposed approach is evaluated by simulation in different operating conditions. To this purpose some performance indexes are considered, such as: the frequency deviation, the stability of the DC bus voltage and the AC voltage total harmonic distortion.

Electricity load decomposition prototype for household appliances: System Design and Development

The growing interest in green building is driving the development of energy efficiency building models. One of the essential elements is an effective and efficient Energy Management system. Traditionally, the system contains lots of meters measuring each appliance. Non-Intrusive Load Monitoring (NILM) is a technique for estimating power consumption of each appliance by disaggregating the end-use power consumption measured by a single meter. However, existing NILM are facing two main problems: 1) some of the small appliances and the similar appliances cannot have their power consumption estimated, and 2) NILM requires a long learning process, which means that a new learning process might be needed if a new appliance is connected. To tackle these problems, this paper proposes to use Intrusive Signature (IS) and Power Line Communication (PLC) for circuit-level load decomposition. The paper focuses on the development of a device using frequency domain signature as the IS. The prototype is developed and deployed on the circuit. The results show an accuracy between 83% to 93%.

Household Energy Demand Management Strategy Based on Operating Power by Genetic Algorithm

Effective and adaptable household energy management system needs to be established to promote and implement demand response projects in smart grids. The current household energy demand management strategy cannot provide users with a choice to ensure user comfort, its time sampling accuracy is not high enough, and the operation using the rated power results in a large deviation from the actual cost. In order to solve these problems, this paper proposes an optimization control strategy to achieve the minimum electricity cost based on the user response, equipment operating power, and dynamic pricing. The genetic algorithm is used for calculating the optimal operating parameters of each equipment by using the operating power. The correctness and the high accuracy of the algorithm are verified by comparing with the loop search optimization algorithm. The results show that the daily electricity cost is reduced by 29.0%, and the peak-to-average ratio is reduced by 36.2% after adopting the proposed strategy.

An Enhanced Approach of Artificial Bee Colony for Energy Management in Energy Efficient Residential Building

The residential sector consumes the largest amount of total energy produced by different energy production resources. Therefore, an effective energy management and control system is required for residential buildings to utilize the energy resources in an efficient manner. The primary objective of energy management and control system is to improve the energy efficiency and achieve the occupant’s preferred indoor environmental comfort. Many approaches have been proposed in the literature to effectively manage the energy systems in residential buildings. In this paper, a new approach of Artificial Bee Colony with Knowledge Base (ABC-KB) is proposed for the management of power and occupant’s preferred environment inside the residential building. The complete energy efficient and user-friendly model has different components in which ABC-KB was used for the optimization purpose, whereas the status of different actuators was controlled using fuzzy controllers. The experimental results show that the developed system significantly enhances the energy efficiency and occupant’s comfort inside the residential building as compared to some previously proposed approaches. The model has shown efficiency in achieving high comfort index along with the minimized energy consumption.

総合大学における実効あるエネルギー管理システムに関する研究 : 運用実態調査に基づいた個別分散空調機の設計・運用管理の合理化検討

総合大学における実効あるエネルギー管理システムに関する研究 : 運用実態調査に基づいた個別分散空調機の設計・運用管理の合理化検討

Multi‐agent transactive energy management system considering high levels of renewable energy source and electric vehicles

The future smart grids (SGs) consist of considerable amount of renewable energy sources (RESs), electrical vehicles (EVs), and energy storage systems (ESSs). The uncertainties associated with EVs and uncontrollable nature of RESs have magnified voltage stability challenges and the importance of an effective energy management system (EMS) in SGs as a practical solution. This study presents a multi-agent transactive energy management system (TEMS) to control demand and supply in the presence of high levels of RESs and EVs, and maximises profit of each participant in addition to satisfying voltage regulation constraints. For this purpose, a real-time pricing is considered based on Cournot oligopoly competition model for demand and merit order effect for production to compensate RESs’ fluctuations in real time by an indirect control method. Simulations are conducted in the modified IEEE 37-bus test system with 1141 customers, 670 EVs, two solar plants, four wind turbines, and one ESS. The results show that the proposed multi-agent TEMS can indirectly control EVs, elastic loads, and ESSs to balance the RESs oscillation, minimise customers cost, and regulate voltage in a real-time manner.

Efficient energy management for a grid‐tied residential microgrid

In this study, an effective energy management system (EMS) for application in integrated building and microgrid system is introduced and implemented as a multi-objective optimisation problem. The proposed architecture covers different key modelling aspects such as distributed heat and electricity generation characteristics, heat transfer and thermal dynamics of sustainable residential buildings and load scheduling potentials of household appliances with associated constraints. Through various simulation studies under different working scenarios with real data, different system constraints and user's objectives, the effectiveness and applicability of the proposed model are studied and validated compared with the existing residential EMSs. The simulation results demonstrate that the proposed EMS has the capability not only to conserve energy in sustainable homes and microgrid system and to reduce energy consumption costs accordingly, but also to satisfy user's comfort level through optimal scheduling and operation management of both demand and supply sides.

Efficient Vision-based Smart Meter Reading Network

For building the big data collection infrastructure, a vision-based smart meter-reading network and its gateway is provided for a community gas supply system that uses traditional mechanical meters. In the network architecture, the gas meter readings are captured by embedded image sensor nodes and then transmitted to a newly designed gateway for image recognition and are collected in the embedded database of gateway. The Web-based monitoring system designed using HTML5 is applicable to a mobile device which allows a user to monitor household gas consumption and history and allows a gas company to develop an effective energy management system to analyze community users' energy consumption models using the big data collected in the database.

An approach to evaluate resource utilization in energy management systems

ABSTRACTOrganizations implement management systems to achieve better performance in different functions. One important area of management systems is “energy management systems”. ISO 50001:2011 proposed a systematic framework for operation of an effective energy management system (EnMS). Resources should be used efficiently to obtain suitable performance. Thus, it is useful to evaluate resource utilization by considering the importance and efficiency of resources in processes. In this study, major functions of an EnMS are identified in accordance with ISO 50001. Then, for each function, processes and resources are specified and classified. Later, data are gathered, and appropriate measures are calculated. Finally, data analysis techniques are applied to propose strategic decisions for improvement. In addition, an oil refinery is considered as a case study.