Automated Energy Management Systems Research Articles

The positive and negative impact of novel utility demand response programme and stochastic utility-driven events on renewable penetration and flexibility activation of train and station systems

Integrating an incentive-driven demand response program (DRP) into a metro railway system can enhance the techno-economic benefits of grid operators and metro railway operators by modulating the grid interactions. To facilitate demand response (DR), all energy flexibility resources within the metro network, such as heating, cooling, electrical, and vehicle systems, should be individually regulated by optimal control strategies. Therefore, an automated energy management system was developed and implemented to prioritise and logically control various energy flexibility resources to maximise the annual performance based on the requirements of the DR signal from the utility grid. Automated DR management helps to achieve an annual DR incentive of 0.57 × 106 HKD, enabling a 2.9 times lower annual operational cost of 0.28 × 106 HKD compared to the reference, even after accounting for the annual electricity tariff of 0.84 × 106 HKD. The techno-economic impacts of becoming a DR participant were stochastically studied using Hong Kong’s DRP by examining all crucial factors in the DR business model. In addition, this study defines a methodology for determining the optimal demand reduction target without biasing any stakeholders. Then, the DR business model was comparatively evaluated between a prosumer-based energy system and the same system without renewable energy support. The former system loses the DR incentive in proportion to the load matching compared to the latter system. Moreover, the DRP was extended from static to dynamic DR support, and the results show that considering dynamic DR can further increase the economic benefit of all stakeholders.

Enhanced Converter Control of a Stand-Alone Multilevel Photovoltaic System Featuring a Protection and Supervision System

The focus of this paper is to optimize the control of a stand-alone discretized photovoltaic system and to reduce the power losses. High power installations require discretized photovoltaic systems. This architecture allows to avoid penalizing the entire system during a malfunction. However, its problems lie in the control of the converters of the adaptation stages and the monitoring of the global system in case of failure. The paper presents, on the one hand, an optimized control algorithm based on the Hill-Climbing method that takes into account the characteristics of the photovoltaic panels and the batteries used to improve the performance and reliability of the photovoltaic system, and, on the other hand, an automated energy management system and a remote supervision interface of the installation to ensure interaction with the site. The integration of the main blocks in our photovoltaic chain has guaranteed continuity of energy production, protection of equipment and reduction of intervention time in case of anomaly in the system. All the results obtained showed that the efficiency of the control of the adaptation stages is around 96%. The average relative error between the simulation and the experiment is 2.47%. After a daily measurement, the efficiency of the MPPT control (98.7%), the efficiency of the adaptation stages (88%) and the efficiency of the overall system (85%) are satisfactory and close to the optimal values of the overall system.

Evaluating the impact of technological renovation and competition on energy consumption in the workplace

This study investigates the effect of two interventions aimed at reducing electricity consumption among branches of a large Italian bank. The first intervention consists in the technological renovation of 70 branch buildings through the installation of an automated energy management system. The second is an energy-saving competition that involved more than 500 branches for a year. Using two separate difference-in-differences estimations, we find that the technological renovation curbs electricity consumption by 15.8 percent overall, and by more than 25 percent outside the main work schedule. The behavioral intervention reduces electricity consumption, by around 6 percent outside the main work schedule, and by 2.4 percent overall, although not significantly so. The estimated cost-effectiveness ranges between 3.4 and 8.8 € cents per kWh saved for the technological intervention, and 9.8 and 17.8 € cents per kWh saved for the behavioral intervention. Our findings suggest that for both interventions, energy savings in the workplace are more easily obtained by reducing passive energy waste than through behavioral change during working hours.

AI agents envisioning the future: Forecast-based operation of renewable energy storage systems using hydrogen with Deep Reinforcement Learning

Hydrogen-based energy storage has the potential to compensate for the volatility of renewable power generation in energy systems with a high renewable penetration. The operation of these storage facilities can be optimized using automated energy management systems. This work presents a Reinforcement Learning-based energy management approach in the context of CO2-neutral hydrogen production and storage for an industrial combined heat and power application. The economic performance of the presented approach is compared to a rule-based energy management strategy as a lower benchmark and a Dynamic Programming-based unit commitment as an upper benchmark. The comparative analysis highlights both the potential benefits and drawbacks of the implemented Reinforcement Learning approach. The simulation results indicate a promising potential of Reinforcement Learning-based algorithms for hydrogen production planning, outperforming the lower benchmark. Furthermore, a novel approach in the scientific literature demonstrates that including energy and price forecasts in the Reinforcement Learning observation space significantly improves optimization results and allows the algorithm to take variable prices into account. An unresolved challenge, however, is balancing multiple conflicting objectives in a setting with few degrees of freedom. As a result, no parameterization of the reward function could be found that fully satisfied all predefined targets, highlighting one of the major challenges for Reinforcement Learning -based energy management algorithms to overcome.

Modelling state spaces and discrete control using MILP: computational cost considerations for demand response

INTRODUCTION: Demand response (DR) has been proposed as a mechanism to induce electricity cost reductions and is typically assumed to require the adoption of time-differentiated electricity prices. Making the most of these requires using automated energy management systems to produce optimised DR pl

Internet of things and cloud computing‐based energy management system for demand side management in smart grid

A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load-shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state-of-the-art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing-based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many-to-many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal.

Local Estimation of Critical and Off-Peak Periods for Grid-Friendly Flexible Load Management

In the demand-side management (DSM) context, some appliances are remotely controlled by the utility or by end-users based on the expected aggregated consumption profile or day-ahead electricity rates. The main objective of these actions is to shift the load from critical to off-peak periods. This article proposes a novel approach to estimate in real time the load as seen by the distribution power network. The only requirement to perform this online estimation is the voltage measurement at the electrical board panel of the end-user building. The proposed method combines digital filtering, statistical process, and transient analysis to make possible the accurate identification of the corrective actions introduced by the voltage regulation system of the distribution network. Hence, this identification permits the estimation of the aggregated load profile. The method provides real-time information that can be used by the end-user or an automated energy management system to initiate demand response actions depending on the state of the grid, e.g., shaving, shifting, or modulation of the local load. Experimental data of three different locations permitted the validation of this novel approach, which enables the implementation of grid-friendly home energy managements systems with DSM functions at a very low cost for the utilities and end-users.

Measuring The Time Course of Engineer Workload During Automation Mode Transitions

In current freight railroad operations, locomotive control is shared between the human engineer and automated energy management systems such as GE Trip Optimizer. Transitions between the manual and automatic modes temporarily increase engineer workload above the demands of the current operating situation. Using the average response time and average number of secondary visual attention task stimuli presented as proxy measures of workload, we tracked the time course of workload during the mode transitions for two types of automated control system. The average response times peaked immediately after the mode change. The average number of stimuli presented peaked just before the mode change. We conclude that this technique of measuring workload would be very useful in evaluating future automated systems concepts in a train simulator.

Reap the Benefits of Automated Energy Management Systems

Reap the Benefits of Automated Energy Management Systems

Automation of energy management is the key to reduce product cost

Subject:automated energy management systems.Purpose:identification of the main prospects for the use of automated energy management systems.Materials and methods: in Russia each year, due to the scornful attitude towards energy saving, about 400 million tons of reference fuel are lost, while only every third company approaches the energy efficiency systemically. Savings of fuel and energy resources largely depend on the proper organization and feasibility of rationing fuel and energy costs. One of the priorities of the strategy of transition to energy-efficient production is to carry out systematic work on the implementation of a system of resource and energy saving — the formation of the energy policy of the enterprise. The introduction and development of energy metering systems (AIIS TUE) for the sake of increasing the transparency of consumption and distribution of energy resources is an integral part of the implementation of the company’s energy policy. Minimizing the influence of the human factor at all stages is easily amenable to mathematical description and, accordingly, automation. The paper shows that without automation of management processes and monitoring energy efficiency it is impossible to count on success. The main stages of the implementation of the energy management system at the enterprise are given.Results: it is shown that the main result of the introduction of an automated energy management system is to reduce costs by more than 10 %, with the identified potential for further savings of at least 15...20 %, which is achieved by cre­ating a completely transparent energy consumption environment, with its characteristic reliability determined efficiency of the processed information.Conclusions:automated energy management can become the core in which all information flows from all management systems that have an impact on energy efficiency will be consolidated. Properly constructed automated energy manage­ment systems are a universal tool for implementing the energy policy of an enterprise, a serious investment with a clear payback horizon and an positive financial result over a long period.

A Non-Intrusive Heuristic for Energy Messaging Intervention Modeled Using a Novel Agent-Based Approach

In response to the increased energy consumption in residential buildings, various efforts have been devoted to increase occupant awareness using energy feedback systems. However, it was shown that feedback provided by these systems is not enough to inform occupant actions to reduce energy consumption. Another approach is to control energy consumption using automated energy management systems. The automatic control of appliances takes-out the occupant sense of control, which is proved to be uncomfortable in many cases. This paper proposes an energy messaging intervention that keeps the control for occupants whilst supporting them with actionable messages. The messages inform occupants about energy waste incidents happening in their house in real-time, which enables occupants to take actions to reduce their consumption. Besides, a heuristic is defined to make the intervention non-intrusive by controlling the rate and time of the messages sent to occupants. The proposed intervention is evaluated in a novel layered agentbased model. The first layer of the model generates detailed energy consumption and realistic occupant activities. The second layer is designed to simulate the peer pressure effect on the energy consumption behaviour of the individuals. The third layer is a customisable layer that simulates energy interventions. The implemented intervention in this paper is the proposed non-intrusive messaging intervention. A number of scenarios are presented in the experiments to show how the model can be used to evaluate the proposed intervention and achieve energy efficiency targets.

University energy management improvement on basis of standards and digital technologies

Nowadays to implement the energy management system it is important to fulfill not only the legal requirements but also to follow the set of recommendations prepared by international and national management standards. The purpose of this article is to prepare the concept and methodology for the optimization and improvement of the energy management system (EMS) for Universities with implementation of legal requirements and recommendations from international and national management standards with the help of digital technologies. During the research the systematic analysis, complex approach, logical sampling and analogy were used. It is shown that this process should be done with the help of the process-based approach, in accordance with ISO 9001, and energy management ISO 50001. The authors developed the structure of the basic standard of energy management: "Guidelines for the energy management system". It is proved that the involvement of the technical senior students in the project of EMS improvement allows to expand their competencies for new technics and technologies. Cloud service Bitrix24 was chosen for IT-support of the project. During the study, a list of characteristics was used as a basis for creating a query to the technology department of the university. DBMS Microsoft Access was chosen for its creation. In addition, the possible results of initiating a single database containing all the information needed for accounting and control of energy supply were listed. Moreover, the possibility of automated energy management system implementation and its results were considered. The required actions described in this research can be implemented in any University, that will extend energy management to any University worldwide.

Residential Demand Response: Dynamic Energy Management and Time-Varying Electricity Pricing

Demand response programs are currently being proposed as a solution to deal with issues related to peak demand and to improve the operation of the electric power system. In the demand response paradigm, electric utilities provide incentives and benefits to private consumers as a compensation for their flexibility in the timing of their electricity consumption. In this paper, a dynamic energy management framework, based on highly resolved energy consumption models, is used to simulate automated residential demand response. The models estimate the residential demand using a novel bottom-up approach that quantifies consumer energy use behavior, thus providing an accurate estimation of the actual amount of controllable resources. The optimal schedule of all of the controllable appliances, including plug-in electric vehicles, is found by minimizing consumer electricity-related expenditures. Recently, time-varying electricity rate plans have been proposed by electric utilities as an incentive to their customers with the objective of re-shaping the aggregate demand. Large-scale simulations are performed to analyze and quantitatively assess the impact of demand response programs using different electricity price structures. Results show that simple time-varying electricity price structures, coupled with large-scale adoption of automated energy management systems, might create pronounced rebound peaks in the aggregate residential demand. To cope with the rebound peaks created by the synchronization of the individual residential demands, innovative electricity price structures—called Multi-TOU and Multi-CPP—are proposed.

Demand side management for a residential customer in multi-energy systems

Today, as a consequence of the growing installation of efficient technologies, e.g. micro-combined heat and power (micro-CHP), the integration of traditionally separated electricity and natural gas networks has been attracting attentions from researchers in both academia and industry. To model the interaction among electricity and natural gas networks in distribution systems, this paper models a residential customer in a multi-energy system (MES). In this paper, we propose a fully automated energy management system (EMS) based on a reinforcement learning (RL) algorithm to motivate residential customers for participating in demand side management (DSM) programs and reducing the peak load in both electricity and natural gas networks. This proposed EMS estimates the residential customers’ satisfaction function, energy prices, and efficiencies of appliances based on the residential customers’ historical actions. Simulations are performed for the sample model and results depict how much of each energy, i.e. electricity and natural gas, the residential customer should consume and how much of natural gas should be converted in order to meet electricity and heating loads. It is also shown that the proposed RL algorithm reduces residential customer energy bill and electrical peak load up to 20% and 24%, respectively.

Optimal Demand Response Using Device-Based Reinforcement Learning

Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem, and argue that this RL problem can be approximately solved by decomposing it over device clusters. Compared with existing formulations, our new formulation (1) does not require explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables the EMS to self-initiate jobs, (3) allows the user to initiate more flexible requests and (4) has a computational complexity linear in the number of devices. We also demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.

Автоматизированный энергоменеджмент теплоэнергетического комплекса университетского городка

This paper describes a structure of automated energy management system for heat and power system of university campus. The system includes SCADA, automated information system for energy efficiency analysis and enterprise management system for heat and power system. Methodical aspects of subsystem for energy efficiency analysis are shown. The paper considers a method for calculation of heat energy consumption limits to monitor energy efficiency in heat and power system. The method considers the requirements of International Performance Measurement and Verification Protocol. If consumption limit calculated using the method is complying during the whole reporting period, energy savings will be statistically significant at the end of the reporting period. The case study of method implementation for a building in South Ural State University campus is presented.

A New Intelligent Neuro–Fuzzy Paradigm for Energy-Efficient Homes

Demand response (DR), which is the action voluntarily taken by a consumer to adjust amount or timing of its energy consumption, has an important role in improving energy efficiency. With DR, we can shift electrical load from peak demand time to other periods based on changes in price signal. At residential level, automated energy management systems (EMS) have been developed to assist users in responding to price changes in dynamic pricing systems. In this paper, a new intelligent EMS (iEMS) in a smart house is presented. It consists of two parts: a fuzzy subsystem and an intelligent lookup table. The fuzzy subsystem is based on its fuzzy rules and inputs that produce the proper output for the intelligent lookup table. The second part, whose core is a new model of an associative neural network, is able to map inputs to desired outputs. The structure of the associative neural network is presented and discussed. The intelligent lookup table takes three types of inputs that come from the fuzzy subsystem, outside sensors, and feedback outputs. Whatever is trained in this lookup table are different scenarios in different conditions. This system is able to find the best energy-efficiency scenario in different situations.

Performance Evaluation of Wireless Home Automation Networks in Indoor Scenarios

Transmission of control information in home local networks is gaining increasing importance. Especially in the context of smart grids several applications already exist and evolve that demand the transmission of control information in home networks. Important examples are the so called smart homes and intelligent buildings, where household appliances and sensor devices are interconnected. They are remotely controlled by an automated energy management system (EMS) according to the demands of the overlying smart grid. This paper presents a methodology for the evaluation of wireless smart homes and home automation networks (HANs) in indoor scenarios. The methodology is applied to investigate the performance of actual wireless technologies that can be used to interconnect devices in a smart home environment. The technologies are compared with each other by investigating the performance in realistic European indoor scenarios. Additionally, they are evaluated with respect to their reliable indoor coverage range. The main intention of this paper is to provide a decision guideline, which wireless standard is most suitable for a certain smart home scenario.

Knowing when to act: an optimal stopping method for smart grid demand response

A major benefit of the smart grid is that it can provide real-time pricing, which enables residential electricity customers to reduce their electricity expenses by scheduling their appliance use. A commonly utilized technique is to operate electrical appliances when the price of electricity is low. Although this technique is simple in principle and easy to apply, there are several issues that need to be addressed: Studies have shown that residents do not know how, or have the time to take advantage of real-time price information; residents seek to save money by delaying device usage but do not want the inconvenience of long wait times; and a lack of automated energy management systems - industry trials of real-time pricing programs requiring manual user intervention have performed poorly. In this work, we address these issues by the means of an optimal stopping approach, which can balance electricity expense and waiting time. We formulate the problem of deciding when to start home appliances as an optimal stopping problem, and combine optimal stopping rules with appliance energy usage profiles. Our result is an automated residential energy management and scheduling platform, which can reduce energy bills and minimize peak loads. Reduced peak loads result in lower usage rates of dirty coal-based peaker plants, reducing carbon emissions. Additional benefits include lower domestic energy use by controlling vampire loads. Simulation results show that our approach can reduce energy costs by about 10-50 percent depending on the appliance type, and demonstrate the usefulness of our approach in managing residential energy costs.

Taking Advantage Of Real-Time Pricing

Linking RTP with automatic energy management and control systems can create win-win scenarios for utilities and their commercial/industrial customers. Widely offered by utilities in the United States and abroad, real-time pricing (RTP) is a proven method of reducing energy costs for a broad range of commercial and industrial customers. Utilities in turn benefit directly by retaining key customers. Now EPRI is leading efforts to make RTP even more attractive to power suppliers and consumers alike. Linking automated energy management systems in customers` commercial buildings and industrial facilities to hourly price signals from the utility can be beneficial to both parties, as shown in several recent EPRI-sponsored demonstrations. With industry deregulation, as new entities like energy service companies change the way electricity is offered to customers, RTP is likely to become part of the vocabulary of more power providers, service providers, regulators, and end users.