Demand-side Energy Management System Research Articles

Design of type-2 fuzzy logic controller in a smart home energy management system with a combination of renewable energy and an electric vehicle

Home energy management is one of the most important parts of a smart home that manages the efficient use of energy in the smart home. This paper aims to design two type-2 fuzzy logic controllers in the demand-side energy management system. For this purpose, a combination of renewable energy sources, such as fuel cells, photovoltaic solar panels, vertical axis wind turbines with a helical savonius rotor, and an electric vehicle and energy storage system along with an external grid are utilized to supply the electricity usage of home appliances in a smart home. By using the proposed method, higher quality, more economical, and environmentally friendly energy can be accomplished. To this end, the outputs of the proposed controllers are defined to make appropriate decisions about the generated energy, how to supply energy for own consumption, and controllable loads. The number and distribution of membership functions of type-2 fuzzy logic controllers are selected according to the measured real input data over a year in Tehran, Iran. To demonstrate the effectiveness of the proposed method it is simulated by MATLAB/Simulink software, which indicates that the proposed system causes the smart home to receive 49.186 kWh less electricity energy from the grid to supply daily power for home appliances and therefore can weekly consume approximately 343.95 kWh less electricity energy from the grid. it is shown that after applying the proposed strategy, electricity costs were reduced by 71.5%, and the peak-to-average ratio was reduced by 64.6%.

Development of fuzzy logic-based demand-side energy management system for hybrid energy sources

Demand-side energy management techniques, such as load shielding, shifting, and delaying appliance operation during peak periods, are typically used to reduce electricity costs at the expense of users’ comfort. To address these challenges, especially where operational delays are unacceptable, distributed generation (DG) is frequently incorporated into the grid system to improve power balance and total energy costs. However, dynamic load demands and varying outputs from renewable DG sources such as solar photovoltaic (PV) systems make energy management in microgrids (MGs) extremely challenging. Moreover, most of the existing studies in this domain focus on objective functions that are geared on optimising the economic balance between cost and value of MG operation over a certain time period. Nevertheless, research that took into consideration the stochastic behaviour of DG’s subsystems in addition to cost and benefit of MG operation are still limited. This current study proposed a fuzzy logic control (FLC) integrated energy management system (EMS) for commercial loads with hybrid grid-solar PV/battery energy system. The proposed technique intelligently selects energy sources using the grid energy cost and the state of charge (SoC) of the solar PV/battery at any time of the day. The EMS operate the loads at a reduced cost without any operational delay or shifting. The system was implemented in the MATLAB/Simulink environment, and the techno-economic feasibility of energy cost savings was investigated by comparing the developed scheme with the Homer hybrid energy system model for a hotel building. The developed EMS reduced energy costs by an average of 11.87 % per day and 7.94 % over a 20-year lifetime.

Modeling and implementation of demand-side energy management system

Modeling and implementation of demand-side energy management system

Adaptive Control of Energy Storage Systems for Real-Time Power Mediation Based on Energy on Demand System

The concept of i-Energy as a new smart demand-side energy management system is proposed, which can realize the versatile and efficient control of e-power flows between distributed generators, numerous appliances, and energy storage systems in the home domain, factories, offices, and local communities. The Energy on Demand (EoD) system is proposed, which is the automatic power control and management system that supplies power to home appliances based on the power demand requests issued from the home appliances. The EoD system can guarantee the reduction in total power consumption by implementing a ceiling control while keeping the quality of life (QoL) of the home user considering the limitation of power supply. This paper proposes an adaptive battery storage management and control method based on the EoD system, which we call the “storage-supported EoD system”. In particular, the storage-supported EoD system can handle multiple power generators, including storage batteries. The overall goals of this paper are not limited to the extension of multiple power supplies only; rather, it provides additional contributions, which are (i) extend the existing power consumption control of home appliances and peak demand shift control (i.e., EoD system) by adding a second power source, i.e., a storage battery system; (ii) propose adaptive storage system design and management for the EoD system; (iii) realize minimum storage capacity for large peak power consumption; and (iv) minimize the home user’s discomfort level due to the limited power supply of one power source. The simulation results have shown the effectiveness of the proposed algorithm with a couple of experiments using real-life data in the smart apartment room. Additionally, simulation results are presented to compare and evaluate the proposed system performance with the EoD system.

Design and Performance Evaluation of a Home Energy Management System for Power Saving

In the context of the ongoing global warming, with environmental concerns regarding the greenhouse gas emissions due to our increasing energy consumption, smart energy management solutions have gained popularity as they have the potential to reduce our impact on the environment and also on our budgets. This paper proposes one of the most affordable designs for an autonomous, microcontroller-based demand-side energy management system to be installed in a home environment where it reduces the standby power consumed by the controlled devices. As a secondary function, it monitors and controls the lights to further save energy. The proposed system is designed to operate independently and also to limit the new wireless sources of electro-magnetic radiation introduced in the home environment. Six homes have been analyzed in terms of the measured energy consumption and to evaluate the energy management capabilities of the system, a prototype was built and tested. Promising results have been obtained and are detailed in the Results and Conclusion sections. A very low purchase price and good performance make this design a viable solution for intelligent home energy management, in today’s economic context.

Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing

This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.

Stochastic receding horizon control minimizing mean-variance with demand forecasting for home EMSs

As demand-side energy management system (EMS) has not only a function to maximize owner's utility by automatic control but also a potential capability to respond a demand activation, demand-side EMS is expected to fulfill the key role for intelligent control of community energy management. To achieve the stable control result of EMS against the uncertain environment, it needs approach from both forecast and operation sides. The objective of this study is to construct a control scheme for a home EMS from a practicable implementation perspective and to evaluate the control performance of the scheme proposed. The scheme is composed of operational planning with receding horizon and energy demand forecasting. The goal of the scheme is to maximize householder's utility only by retrofitting the home EMS to a residential household. Throughout numerical results, it is revealed that the better control performance comes from the conservative operational strategy derived from the multiple scenarios.

Demand Side Energy Management for Linear Programming Method

This paper presents an optimization method to the Demand Side Energy Management System (DSEMS) of a given consumer (e.g.an industrial compound or university campus) with respect to hourly electricity prices. The demands can be supplied through the main grid and stochastic Distributed Energy Resources (DERs), such as wind and solar power sources. To solve this DSEMS problem and optimization algorithm based on Linear Programming (LP) approach has been implemented. The objective of the proposed method is to maximize the utilization of the cluster of demands. This LP algorithm allows the cluster of demand to buy, store and sell energy at suitable times to adjust the hourly load level. To evaluate the performance of the proposed algorithm an IEEE 14 bus system was considered. The results shows that the cluster of demands of energy management system using the proposed approach increasing the efficiency and minimizing the losses than the existing methods. DOI: http://dx.doi.org/10.11591/telkomnika.v14i1.7570

An Implementation of Demand Side Energy Management for Refined Load Control

Demand Side Energy Management System (DSEMS) manages energy demand by controlling end-use appliances in a refined, energy-saving, cost-efficient and user-friendly way. The DSEMS runs on an Android tablet computer, which serves as energy gateway to communicate with two types of controllers via ZigBee network. Smart sockets connected to the ZigBee network will monitor and control plug-in loads and IR remote controller (IRRC) for air-conditioner temperature setting. The proposed system has been installed in an apartment with over 24 rooms as a paradigm, which proved that the DSEMS can realize the autonomous energy-saving via real-time surveillance and control on household appliances.

An Efficient Networked HVAC Controlling System by Using Multi-Mode Concentrator P-Cycle

Autonomous and distributed demand-side energy management system is desired as an electric load management technique that aims to shift the pattern of device energy use. In order to improve environmental condition and achieve energy saving purpose, a distributed controlling mathematic model of the HVAC system is proposed. In this paper, a p-cycle supported multi-mode concentrator with WIA, TD-LTE, McWiLL communication capabilities is designed, and detail networked controlling system coding procedure is illustrated. The multimode concentrator p-cycle (MMCP) can provide strong restorability against multiple span failures that takes advantage of duplicated copies on disjoint path. The efficient networked HVAC controlling system is proposed to solve the problem of two-way interaction between the user side and grid side by using preconfigured cycle protection technology. We also evaluated the performance of the networked controlling system with various classical p-cycles enumeration algorithms, and the experimental results show the eff.

Intelligent demand side energy management system for autonomous polygeneration microgrids

Autonomous polygeneration microgrids is a novel approach in addressing the needs of remote areas. These needs can include power, fuel for transportation in the form of hydrogen, potable water through desalination and space heating and cooling. This approach has been investigated technically and economically and has proved viable. Further research has taken place in the supervisory management of this topology using computational intelligence techniques like fuzzy logic, which has optimized the concept minimizing the sizes of the installed components. The optimal design of the system can meet, though, only the design principles and needs. In reality experience has shown that most autonomous power systems operate out of specifications very shortly after installation or after a couple of years new needs arise and it is not possible economic wise for the people to extend it. In these cases the microgrid would struggle to cover the increased needs and in the end fail, causing blackouts. A solution to this is partial load shedding in an intelligent manner. This paper presents a multi agent system for intelligent demand side management of the polygeneration microgrid topology which also includes grey prediction algorithms for better management. This approach can also be used for designing the optimal polygeneration microgrid for a given amount of an investment. The results show that the proposed intelligent demand side management system can address its design principles successfully and guaranty the most effective operation even in conditions near and over the limits of the design specification of the autonomous polygeneration microgrid.

Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.

Design and commissioning of a valve-regulated lead/acid battery energy-storage system for backing up critical environmental loads

Momentary and sustained electrical power interruptions and voltage depressions represent two of the most difficult and important power quality and delivery problems that face many industrial and commercial users. There is a definite need at many industrial processing plants and commercial users of electrical power to have a dependable, efficient and controllable source of real and reactive power that is available instantly to support large electrical loads (greater than 5 MVA), even if the incoming utility a.c. connection is lost. When power is interrupted or lost, the results can be extremely disruptive for critical processes and cause lost production, costly downtime and loss of customer good will, and in certain industries, can lead to environmental damage through the release of toxic emissions into the air. Recently, this challenge was faced by GNB Technologies at its lead reclaiming and smelting facility in Vernon, CA, USA. This study describes a versatile, cost-effective, workable solution to the problem that has resulted in the design and installation of a 5 MVA, 3.5 MWh battery energy storage system (BESS) which provides uninterruptible power to the critical environmental control equipment throughout the plant. The BESS at Vernon provides the required power, combined with both voltage and frequency control, to allow the plant to tolerate disconnection from the utility grid without suffering unacceptable impacts on critical loads. The system also provides the company with a demand-side energy management system for conducting daily peak shaving of energy demand and, thereby, reduces its electrical bills.