Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm

Demand response is proposed as a solution to handle the fluctuations in the power supply in a scenario with higher penetration of renewable energy sources. While demand response already offers a positive business case in certain domains it still lacks matureness in other areas, especially in the residential domain. This paper aims at providing a feasibility study of residential demand response by designing and validating a novel multi-modal Building Energy Management System (BEMS) that enables demand response provision and energy efficient control from residential buildings. The proposed consumer-centric BEMS monitors the building performance and its surroundings, interacts with the residents, optimally controls local Distributed Energy Resources (DERs) and provides demand response to an aggregator. The design decisions and the consequent architecture are detailed. A prototype of the envisioned BEMS has been developed and deployed in a testbed - a 12-storey residential building located in Denmark. The prototype performance, the data monitoring capabilities, interaction with the residents and controllability of local DER of the BEMS are demonstrated.

Energy systems are essential part of buildings and facilities, which are associated with high costs, and considered key success factor of businesses and services produced from the building or facility. Energy management will protect the underlying business by allowing accurate and automated management of energy systems and supply. Energy management systems (BEMS) are computer‐based automated systems that monitor and control all energy‐related systems from mechanical and electrical equipment in buildings. Building management systems (BMS) are commonly used to automate all services and functions within the building, which include energy management. BMS connects building components with a central computer to enable the control of different variables and parameters within the building. This chapter presents energy management system (EMS) and possible ways to achieve energy monitoring, savings, and smart homes. Case studies will be discussed to analyze energy savings and reduce electricity bills. To achieve full deployment and integration with smart grids, it is essential to implement BMS and BEMS.

Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVAC?s primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyberphysical systems (smart cities) and evolve and interact with their surroundings (Figure 1) [2]. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting [3], [4]. Thus, even in cases of ?ideally? designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult [1], [5]. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.

The available Building Energy Management Systems (BEMS), although they contribute to a significant reduction of energy consumption and improvement of the indoor environment, they can only be implemented in new buildings. Their installation in existing buildings is far from being cost effective due to the incompatibility of communication protocols between BEMS designed by various manufacturers and unavoidable modifications for data transmission. On the other hand, current research for energy efficient buildings has proved that although the design and the facilities including BEMS aim to satisfy the thermal and visual comfort plus the air quality demands while minimising the energy needs, they often do not reach their goals due to users interference. Latest trends in designing Intelligent Building Energy Management Systems (IBEMS) offer a Man Machine Interface that could store the users preferences and adapt the control strategy accordingly. The objectives of the present paper are to present the advantages of the use of a man machine interface based on a smart card terminal together with fuzzy control techniques in satisfying the users preferences plus to underline the capabilities that the LON network offers to the design. A fuzzy PID controller is developed to reach the first of the above objectives. The monitoring of the energy consumption along with satisfying the users preferences is achieved by the use of a suitable cost function for the whole system. All the above parameters as well as the cost function are kept between acceptable limits. The overall control system including the cost function is modeled and tested using MATLAB/SIMULINK. The implementation of the control system in an existing building requires interconnection of sensors and actuators installed across the building, is well served by the LonWorks technology due to its high standards and flexibility features.

In recent years, building energy consumption has increased in response to climate change, resulting in a worldwide reduction of energy efficiency. A strong response is required, through both the enhancement of building energy efficiency and the reduction of power usage. These objectives could be achieved by pursuing “Blackout” prevention, through developing a control unit such as that of the urban energy grid system that was used in the Energy Operation Center (EOC) for smart cities. In this context, this paper presents an optimization operation technique for Building Energy Management Systems (BEMS), using control monitor-based Building Information Modeling (BIM) for the efficient operation of the EOC. BIM is one of the approaches that can be used for the visual representation, management, and exchange of information on all aspects of a building. The EOC should be able to efficiently represent the data from the building information, and the operator maintaining it should be able to promptly acquire the data for handling it. This suggests that the control of energy consumption by a Building Automation System (BAS) to maximize building energy efficiency will lead to improved total energy performance, reduced operating costs, and reduced environmental impact.

Connecting the Last Mile: Demand Response in Smart Buildings

To realize smart grid technologies such as load control, load leveling and demand response and solve problems caused by connecting many DERs to a distribution network, a distribution automation system (DAS) and energy management system (EMS) are respectively essential components. The communication of these systems has generally been handled by proprietary or vendor-specific protocols to date. Conversely, in intra- and inter-substation communication, the IEC 61850 has started to gain acceptance in supervisory control communication, due to its interoperability supporting multi-vendor environments. If the IEC 61850 can be applied to communications in DAS and EMS, this is expected to reduce the maintenance and equipment costs of each system. To do so, we must first evaluate the applicability of the IEC 61850 from the perspective of Logical Nodes (LN) and Data Objects (DO), which are data models defined in IEC 61850. This paper focuses on the DAS and building EMS (BEMS) and provides the evolution. We choose BEMSs because we expect many EMSs to be installed in homes or buildings in the near future and anticipate that building energy management functions will include home energy management functions. This evaluation result shows that most data items appearing on such communications can be represented as standard LNs and DOs but several data items cannot be easily represented as the same.

Building automation and electrical control systems are pivotal in enhancing the efficiency, sustainability, and comfort of modern buildings. These systems integrate a variety of technologies that optimize the use of energy, improve operational efficiency, and provide a comfortable environment for building occupants. This review paper explores the fundamental components and types of building automation systems (BAS), the role of electrical control systems, their applications in residential and commercial buildings, and the benefits they bring in terms of energy efficiency, cost reduction, and enhanced operational control. Moreover, the paper discusses emerging trends, challenges, and future directions, such as the integration of IoT, smart grids, and advanced control strategies in building automation.

The increasing power demand and the emergence of smart grids have given access to new opportunities for the energy management system of smart residential buildings that can reduce energy usage. Demand Response (DR) program is one of the most important strategies for implementing the residential building energy management system. The consumer load gets modified depending upon the electricity price provided by the utility so that the consumer electricity bill decreases. This strategy decreases the peak demand by scheduling the schedulable loads from the period when the electricity prices are high for the period when the prices are low with minimal effect on consumer comfort. It reduces the demand and increases the effective utilization of the energy of a residential building. This paper proposes an Effective Energy Management System (E-EMS) for residential buildings that models the problem by considering the uninterruptable and interruptible residential load appliances which can be controlled and their shifting windows constraints. It also manages the battery bank operation for maximum utilization of energy stored so that the wastage of power through dump load is minimum depending upon the Power Export Limit (PEL) defined by the utility. Further, the optimal sizing of renewable energy resources is also carried out for choosing the size of renewable energy resources and battery banks for effective utilization. The results of the proposed system are simulated by using MATLAB software to verify the effectiveness of the proposed strategy to control household appliances and their energy consumption by using BPSO.

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Building energy management is recognized as a useful technique to enhance building automation and facilitate demand response. This paper proposes a simple and useful building energy management system (BEMS) that simultaneously minimizes the user's electricity bill while assistants the utility to reduce the Peak-to-Average Ratio (PAR) of the regional load. The working mechanism of the proposed BEMS is based on a simple observation: the PAR reduction of an area can be achieved by reducing the PAR of individual buildings. The system consists of two stages: (1) in the first stage, the appliances are temporarily scheduled to obtain the minimum electricity cost of the unit; and (2) in the second stage, the appliances are re-scheduled with the objective to minimize the unit's PAR, while ensuring the building's electricity cost does not exceed the optimal cost determined in the first stage. Our numerical results show that by applying the proposed BEMS to individual units, the overall PAR of a group of buildings can be effectively reduced.

Effect of electrochromic windows on energy consumption of high-rise office buildings in different climate regions of Iran

The distribution company (DISCO) determines optimal retail prices to operate the distribution network efficiently while promoting demand response (DR) programs. In addition, an energy storage system (ESS), which improves peak load management, is widely used for price-based DR. This paper proposes an electricity retail pricing strategy that considers the optimal operation of an ESS using a machine learning algorithm. An artificial neural network (ANN) is used to develop a practical model of the DR scheduling of an ESS. This model is trained using historical data that include the electricity price and the corresponding optimal demand obtained from the building energy management system. The proposed model is replicated using mathematical equations and directly integrated into the constraints of the retail pricing optimization problem of the distribution management system. The proposed ANN-based DR model of the ESS allows the development of an optimal pricing strategy with a single-level structure while reflecting the decision-making process of both the DISCO and the building operator. The proposed ANN-based DR model is verified through case studies, which prove that the model successfully expresses the price-optimal demand function and has high practical applicability. The results of the retail pricing demonstrate that the proposed strategy can accurately determine the balancing points while reducing the peak load.

Per the National Energy Policy, Demand Side Management (DSM) is one of the energy conservations that performs a function to manage electric power of demand-side resources. One of the DSM solutions is a demand response program, which is a part of Thailand Smart Grid Action Plan 2017 - 2021. Demand response program such as peak demand reduction plays a role in both the management of the electricity crisis and enhance energy security. This paper presents a pilot project for a fully automated demand response program at MEA Rat Burana District Office. The system is composed of a Building Energy Management System (BEMS) with Demand Response Client gateway and 5 energy controllers at the air conditioner by using the OpenADR2.0b protocol. Also, this concept leads to automatic or semi-automatic demand response program in the future. The result shows the total energy consumption reduction for air conditioners by 53.5%. The future works to be carried out are to implement into other MEA District Office such as Khlong Toei, Yan Nawa and Bang Khun Thian and to test with a Load Aggregator Management System (LAMS).

As cyber-physical service systems, comfort, safety, customer experience, cost, and the requirement that all are provided simultaneously-thus requiring a smart solution-are thus the fundamental properties of smart malls and hotels. BAS, or Building Automation System, with Building Energy Management Systems being a special case with emphasis on energy, have thus evolved from mere supervisory control to data acquisition, supervisory control, analytics, and, most importantly, a governing model. This systematic study will aggregate all available evidence from 2020 to 2025 on the benefits of Centralized Building Management System as a model for improving operation efficiency in Smart malls and hotels. Our study synthesized all relevant evidence in five key areas described by the individual authors, including energy and carbon, fault detection/diagnostics - maintenance productivity, indoor environmental quality, enterprise integration, cybersecurity, and assurance, by adopting PRISMA 2020 guidelines. Coherently in all ascertained evidence, there seems to be a coherent trend that the key to improved operational efficiency is totally encapsulated in the phrase "analytics to action" since even as there is "interoperable connectivity of all devices allowing high-fidelity visibility," there is room for a "continuous commissioning based on FDD/AFDD, occupancy-based control, and optimization, which must translate into 'governed actions' through CMMS/PMS with measurement & verification." Lately, studies have found Building Energy Management Systems with the aid of digital twin technology to be efficient, as well as Analytic Function Detection Diagnosis with the aid of agent/ontology technology to improve interpretable diagnosis reports, although there are gaps in evidence, with some authors citing the benefits seen by others by virtual modeling or simulation. We present our reference architecture as well as our KPIs, with a matching research requirement that will focus on credible study designs, as well as the use of semantic modeling as well as security by design, by utilizing protocol extension technologies like BACnet SC.