Energy Management And Control Systems Research Articles

Computational Study of Security Risk Evaluation in Energy Management and Control Systems Based on a Fuzzy MCDM Method

Numerous cyberattacks on connected control systems are being reported every day. Such control systems are subject to hostile external attacks due to their communication system. Network security is vital because it protects sensitive information from cyber threats and preserves network operations and trustworthiness. Multiple safety solutions are implemented in strong and reliable network security plans to safeguard users and companies from spyware and cyber attacks, such as distributed denial of service attacks. A crucial component that must be conducted prior to any security implementation is a security analysis. Because cyberattack encounters in power control networks are currently limited, a comprehensive security evaluation approach for power control technology in communication networks is required. According to previous studies, the challenges of security evaluation include a power control process security assessment as well as the security level of every control phase. To address such issues, the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based on multiple criteria decision-making (MCDM) is presented for a security risk assessment of the communication networks of energy management and control systems (EMCS). The methodology focuses on quantifying the security extent in each control step; in order to value the security vulnerability variables derived by the protection analysis model, an MCDM strategy incorporated as a TOPSIS is presented. Ultimately, the example of six communication networks of a power management system is modelled to conduct the security evaluation. The outcome validates the utility of the security evaluation.

A Review of Microgrid Energy Management and Control Strategies

Several issues have been reported with the expansion of the electric power grid and the increasing use of intermittent power sources, such as the need for expensive transmission lines and the issue of cascading blackouts, which can adversely affect critical infrastructures. Microgrids (MG) has been widely accepted as a viable solution to improve grid reliability and resiliency, ensuring continuous power supply to loads. However, to ensure the effective operation of the Distributed Energy Resources (DER), Microgrids need to have Energy Management and Control Systems (EMCS). Therefore, considerable research has been conducted to achieve smooth profiles in grid parameters during operation at optimum running cost. This paper aims to provide a review of EMCS techniques that have evolved in recent years. Firstly, the fundamentals of microgrids are discussed for a general overview of the field. Then, a critical literature review is undertaken for the various methods applied for EM optimization in microgrid applications. Multiple factors have been explored in the objective functions throughout this review, including MG daily operational costs, energy storage degradation, revenue through trading with the grid or other parties, and Greenhouse Gas (GHG) emissions. A review of control systems has been conducted next by categorizing them based on the different applications in MGs for stable operation. This paper also focuses on IEEE standards related to MG operation and control to facilitate other researchers to build upon a standardized set of rules and to enhance the interoperability of the diverse EMCS techniques.

A Scalable Approach to Energy Improvements Using Energy Management And Control Systems

ABSTRACT Energy management and control systems (EMCS) for the commercial building sector have undergone dramatic changes over the last decades. Nevertheless, they provide inadequate assistance to owners and operators when it comes to managing energy. Their prime focus has always been managing equipment rather than managing overall building performance. With a renewed national and international focus on building energy consumption, and ambitious targets set by various governmental agencies, systems now have to shift from managing equipment to the much larger picture of providing a useful tool in the context of managing a national energy policy. This article aims to illustrate some of the challenges faced by the engineering, construction, and building operator community when it comes to meeting new state and federal guidelines on energy efficiency.

A Scalable Approach to Energy Improvements: Using Energy Management and Control Systems

ABSTRACT Energy management and control systems (EMCS) for the commercial building sector have undergone dramatic changes over the last decades. Nevertheless, they provide inadequate assistance to owners and operators when it comes to managing energy. Their prime focus has always been managing equipment, rather than managing overall building performance. With a renewed national and international focus on building energy consumption, and ambitious targets set by various governmental agencies, systems now have to shift from managing equipment to the much larger picture of providing a useful tool in the context of managing a national energy policy. This article aims to illustrate some of the challenges faced by the engineering, construction, and building operator community when it comes to meeting new state and federal guidelines on energy efficiency.

Self-tuning dynamic models of HVAC system components

A great majority of modern buildings are equipped with Energy Management and Control Systems (EMCS) which monitor and collect operating data from different components of heating ventilating and air conditioning (HVAC) systems. Models derived and tuned by using the collected data can be incorporated into the EMCS for online prediction of the system performance. To that end, HVAC component models with self-tuning parameters were developed and validated in this paper. The model parameters were tuned online by using a genetic algorithm which minimizes the error between measured and estimated performance data. The developed models included: a zone temperature model, return air enthalpy/humidity and CO 2 concentration models, a cooling and heating coil model, and a fan model. The study also includes tools for estimating the thermal and ventilation loads. The models were validated against real data gathered from an existing HVAC system. The validation results show that the component models augmented with an online parameter tuner, significantly improved the accuracy of predicted outputs. The use of such models offers several advantages such as designing better real-time control, optimization of overall system performance, and online fault detection.

Artificial Intelligence Approach to Energy Management and Control in the HVAC Process: An Evaluation, Development and Discussion

AbstractComputer control technology in heating, ventilating and air conditioning (HVAC) systems has evolved in two technical phases: central control and direct digital control (DDC) with central monitoring function. Many control strategies have been developed for energy conservation in HVAC energy management and control systems (EMCS) and these are reported in the literature. An artificial intelligence approach to the EMCS in HVAC process is proposed in this paper. Two real time expert systems for energy‐saving operation and indoor setting are developed and integrated with an adaptive control strategy. Further, a configuration of an integrated intelligent system for intelligent building management is proposed, in which building management functions are coordinated by a meta‐system. The applications of artificial intelligence (AI) techniques in a HVAC process give a new avenue for the development of EMCS in the HVAC industry.

Use of energy management systems for performance monitoring of industrial load-shaping measures

We have studied the use of industrial energy management and control systems (EMCSs) for monitoring the performance of electric load-shaping measures in three of California's most electricity-intensive and rapidly growing industrial sectors: food, plastics, and computing equipment and electronics. In this paper, we summarize current load-shaping strategies, report on the current use of EMCSs in selected industries, and recommend ways for electric utility companies to verify the potential of EMCSs for performance monitoring. We conclude that EMCSs can be used to collect and store data for evaluating industrial load shaping. Some sophisticated EMCSs are currently being used for this purpose, mostly in larger electronics firms. Most EMCSs now available need to be customized to monitor a particular facility. We also conclude that electric utility companies can encourage the use of EMCSs for performance monitoring by helping to educate their industrial customers about EMCSs, establishing protocols to standardize communication between EMCSs, and testing EMCSs with data-logging functions at demonstration sites.

An Innovation-Based Methodology for HVAC System Fault Detection

Although Energy Management and Control Systems (EMCS) have since the early 1970’s contributed significantly to the reduction (20-40 percent) of energy use in buildings without sacrificing occupants’ comfort, their full capabilities have not been completely realized. This is in part due to their inability to quickly detect and compensate for failures in the Heating, Ventilation and Air Conditioning (HVAC) system. In fact, no matter how good the control scheme for the HVAC system might be, the presence of undetected faults can completely offset any expected savings. This paper presents a methodology for detecting faults in an HVAC system using a nonlinear mathematical model and an extended Kalman filter. The technique was implemented in a computer program and successfully used to detect “planted” faults in simulations of the air handler unit of an HVAC system. Test results are presented to demonstrate the effectiveness of the methodology.