Blockchain Technology in Energy Management Systems: Enhancing Security and Transparency

The use of knowledge-based systems (KBS) in the development of energy management system (EMS) applications is addressed. Since the user interaction principles in today's EMSs are not considered good enough, a tentative specification for the next generation of EMS applications is proposed. The justification for the use of KBS technology in power system operation is discussed, and a prototype KBS development environment is presented. The goal of this project was to get security analysis tools into daily operation through a user-friendly and powerful man-machine interface. Dedicated hardware (LISP-machine) and sophisticated software tools (expert system shell) were used in the KBS development environment. >

Machine learning based on reinforcement learning for smart grids: Predictive analytics in renewable energy management

This paper reviews the work in the areas of machine learning applications for energy management in smart buildings, 5G technology’s role in smart energy management, and the use of machine learning algorithms in microgrid energy management systems. The first area focuses on the adaptability of building-integrated energy systems to unpredictable changes through AI-initiated learning processes and digital twins. The second area explores the impact of 5G technology on smart buildings, particularly in Singapore, emphasizing its role in facilitating high-class services and efficient functionalities. The third area delves into the application of various machine learning algorithms, such as supervised and unsupervised learning, in managing and monitoring microgrids. These broad areas collectively offer a comprehensive understanding of how machine learning can revolutionize energy management systems in smart buildings, making them more efficient, adaptable, and sustainable.

Worldwide, the current energy system increasingly shows economic, social and environmental drawbacks and it does not appear to be sustainable. Among the most pressing problems are climate change due to CO2 emissions, the depletion of fossil resources, the security of energy supply, and threats to economic development. These problems can be addressed by moving towards energy systems that increasingly rely on renewable and sustainable energy sources. At the same time, the rapid progress in renewable energy harvesting and information and telecommunication technologies offer unprecedented opportunities for energy generation, distribution and management, and to more strongly involve people in the process of energy generation and management, which is needed to further enhance the sustainable energy systems. Indeed, renewable and sustainable energy sources are likely to be produced and distributed in a decentralised manner, and require energy management systems that differ greatly from the current centralised system. Hence, the integration of renewable sources has major implications for the design and management of energy systems, relying on innovative ICT solutions and stronger user involvement that facilitate supply and demand matching. Effective energy management requires the active participation and support of end users, who should accept the energy management systems, and adapt their energy use to the available demand as to enhance the efficiency and stability of the energy system. Hence, the design of future smart grids should be based on sound behavioural models, which should be integrated in ICT centred solutions that facilitate supply and demand matching. More specifically, we need to understand how truly complex information on energy production, use and storage can be effectively communicated among relevant actors, and what type of information and incentives should be provided to realise effective supply and demand matching. In addition, we need to understand how to take into account cultural specifics in the design of energy management systems, to enhance effective energy management across the world. The active and aware involvement of the end-user can only be achieved if a scalable and reliable ICT infrastructure is in place, which naturally interfaces the energy flows with the decision autonomy of the people. Hence, the design of effective energy management systems should be take into account key behavioural as well as ICT requirements in an integrated way. Our proposal is thus to investigate (1) ICT based solutions for monitoring and controlling the power grid focusing on the distribution networks (medium-low voltage) and buildings (offices and residential complexes) specific to the Qatar context; (2) behavioural models on effective and acceptable incentives to match energy supply and demand; (3) cultural specificities in the design of energy management systems by comparing results from research in Qatar to similar research in other parts of the world, in particular Europe. Together, these will provide important insights in how to promote efficient and stable renewable energy systems that meet Qatar's sustainability goals.

Planning of regional energy systems: An inexact mixed-integer fractional programming model

Deep learning-based model predictive control with exponential weighting strategy and its application in energy management systems

With the advent of smart grid concept, Internet of Things (IoT) and the deployment of smart meters, the cyber-attack threats on power networks have increased due to the use of communication systems that can be accessed by adversaries. Attackers will have the ability to manipulate the outcomes of smart meters which in turn influence the core application of Energy Management System (EMS): State Estimation (SE). Bad data analytic tools may fail to detect some attacks into measurements. Meanwhile, Machine Learning (ML) solutions have been proposed for detecting False Data Injection (FDI) attacks. However, there is a lack of ML time-series solutions presented in the state-of-the-art that is yet to be less complex. In signal processing, time-series solutions does not only consider the signal, but also the statistics of the signal over time. Therefore, in this paper, a machine learning for time-series solutions is presented as an application to model the measurements of the power grid that are used in SE. The presented model takes into account adaptive linear and non-linear filters: Finite Impulse Response (FIR), and Infinite Impulse Response (IIR). The presented models are implemented and performed on the IEEE-118 bus system. The results indicate the advantage of applying those filters over the state-of-the-art machine learning solutions.

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper describes a new method for enhancing the accuracy of network analysis applications in energy management systems. Topology processing, state estimation, power flow analysis, and contingency analysis play a key factor in the stable and reliable operation of power systems. In this respect, the aim of topology processing is to provide the electrical buses and the electrical islands with the actual state of the power system as input data. The results of topology processing is used to input of other applications. New method, which includes the topology error analysis based on inconsistency check, coherency check, bus mismatch check, and outaged device check is proposed to enhance the accuracy of network analysis. The proposed methodology is conducted by energy management systems and the Korean power systems have been utilized for the test systems.

The preparation of standards in the field of energy management of both international, regional, and national status by specialists of energy efficiency and energy saving problems, which are actively discussed around the world today, is an urgent problem. In this regard, the ISO 50001 Energy Management System standard allows organizations to prepare a policy package for more efficient use of energy to determine the objectives for the implementation of this policy use available information more effectively to make decisions about energy consumption; measure and evaluate the obtained results to evaluate the effectiveness of the policy continuously improve the energy management system. The main objective of ISO 50001 is to integrate energy efficiency into the existing management practices of organizations. The application of energy management systems as one of the main tools for the implementation of energy saving policy in enterprises based on the requirements of the ISO 50001 standard is very relevant, and the reasons for the need to apply energy management systems and relevant standards are asfollows Energy management system and standards as a component of national energy efficiency and renewable energy programs serves, energy management system accelerates technology improvements and innovations. ISO 50001 helps to develop a strategy to increase energy efficiency in buildings and structures, reduce costs and improve the environmental performance of operations.

The implementation of monitoring tools and energy management systems (EnMSs) supports companies in their long-term energy efficiency strategies, and they are essential to analyse the effectiveness of energy performance improvement actions (EPIAs). The first fundamental step towards increasing energy efficiency is the development of energy audits (EAs). EAs provide comprehensive information about the energy usage in a specific facility, identifying and quantifying cost-effective EPIAs. The crucial role of these tools in clean energy transition is remarked by the European Energy Efficiency Directive (EED), which promotes the implementation of EAs and EnMS programmes. The purpose of this work is to better understand the link between EnMSs (specifically ISO 50001) and EAs in the EED Article 8 implementation in two industrial and two tertiary sectors in Italy. Moreover, the impact of company size, energy monitoring systems, and EnMSs on planned and/or implemented EPIAs is analysed. Our findings show that, albeit the complexity of the variables involved in energy efficiency gap, the “energy savings/company” and “EPIA/site” ratios are higher in enterprises with an EnMS and monitoring system. Thus, a correct energy audit must always be accompanied by a specific monitoring plan if it is to be effective and useful to the company decision maker.

Energy is one of the top operating expenses in industries. Following the increased adoption of smart grids in recent years, industries can leverage on its capabilities to design effective energy management schemes for competitive advantage. This paper addresses the challenge of energy management in industries by incorporating the aspects of a smart grid in designing an energy management system (EMS) where demand side management (DSM) is utilized to enable users control their energy usage and minimize costs. A forecasting model for electricity prices and demand is developed using Long Short Term Memory (LSTM) - Recurrent Neural Network (RNN). The predicted prices are used in load scheduling to realize potential energy cost savings. The nonpriority loads are scheduled to leverage on low electricity prices during off peak times. The effectiveness of the designed energy management strategy is tested using an IEEE 30 bus system. A suitable operation schedule with committed units for each hour is given for one sample day. Using the test system with 20 loads yielded an annual energy cost saving of $2,961,169.20 and a payback period (PBP) of 4.39 years. Quantifying both the energy and non-energy benefits of investing in an EMS justifies its high investment cost. Long term use of an industrial EMS is likely to yield huge energy and cost savings.

Considering the global energy crisis and the need to reduce energy consumption while providing thermal comfort to occupants, building performance prediction using building simulation programs requires higher accuracy of output data. Therefore, it seems necessary to study the impact of occupant behavior, which is the main source of uncertainty in residential buildings. The traditional courtyard houses, which are recognized as a successful passive house model, respond to different climatic conditions. Therefore, this research focuses on this building type to analyze occupant window opening control scenarios and determine which control works better. For this purpose, several probabilistic controls and their effects on the adaptive thermal comfort of occupants in zones around a central courtyard were compared in the three cities of Yazd, Bandar Abbas, and Tabriz. Energy Plus was used as a simulation program for the application of Grasshopper's energy management system (EMS) along with the Ladybug and Honeybee environmental plugins. The results show that the window control algorithms can increase the adaptive thermal comfort of occupants by 25.7%, 32.2%, and 20.3% in each of the climates of Yazd, Bandar Abbas, and Tabriz cities, respectively. Indoor and outdoor temperature were the most significant variables for opening windows in the warm and cold seasons, respectively.

The green industry is an industry that in its production process prioritizes the efforts to efficient and effective using of resources in a sustainable manner so as to be able to harmonize the industrial development with the environmental sustainability and can provide benefits to the community. One of the assessment criteria for the green industry is the energy aspect, namely industrial efforts in implementing energy efficiency, the usage of renewable energy, and conducting energy management activities. This research was conducted based on a case study of the application of Energy Management System at PT. X, which is one of the largest pulp and paper industry in Indonesia. The industry has an impact on reducing CO2 emissions through the implementation of Energy Management System. PT. X can save energy consumption by 47,700 GJ and reduce CO2 emissions by 11,805.75 tons CO2e, in 2017.