Smart Grid Energy Systems Research Articles

Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic Forecasts

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.

A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future

A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future

Load and Household Profiles Analysis for Air-Conditioning and Total Electricity in Malaysia

Load profile of household air-conditioning (AC) and total electricity consumption is essential to increase the stability of the energy demand on the grid. Therefore, field measurements on time series data of total and AC electricity consumption from 20 households were conducted from March 2016 to August 2017. The questionnaire survey was carried out simultaneously to grasp the profile of each family. The average total daily and AC consumption were 14.5 kWh/day and 3.9 kWh/day, respectively. The average hourly electricity consumption for total was 0.6 kWh/hour, meanwhile for AC was 0.2 kWh/hour. About 20% of the total peak demand was contributed by the consumption of AC. The indoor air temperature was measured in the bedroom (BR) when AC was switched ON and OFF with an average of 27 ∘C and 29 ∘C, respectively. However, the indoor air temperature in the living room (LR) was 2∘C and 1∘C higher if compared to BR for both conditions. Based on the questionnaire survey, 92% of the occupants preferred a temperature setting below than the level recommended by the Malaysian standard i.e., 24 ∘C. These results might be beneficial to understand the occupant behavior of electricity demand in Malaysia for designing smart grid energy systems in the future.

Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management.

Privacy, technology, and norms: The case of Smart Meters

Norms shift and emerge in response to technological innovation. One such innovation is Smart Meters – components of Smart Grid energy systems capable of minute-to-minute transmission of consumer electricity use information. We integrate theory from sociological research on social norms and privacy to examine how privacy threats affect the demand for and expectations of norms that emerge in response to new technologies, using Smart Meters as a test case. Results from three vignette experiments suggest that increased threats to privacy created by Smart Meters are likely to provoke strong demand for and expectations of norms opposing the technology and that the strength of these normative rules is at least partly conditional on the context. Privacy concerns vary little with actors’ demographic characteristics. These findings contribute to theoretical understanding of norm emergence and have practical implications for implementing privacy protections that effectively address concerns of electricity users.

Detection and Protection Against Intrusions on Smart Grid Systems

The wide area monitoring of power systems is implemented at a central control center to coordinate the actions of local controllers. Phasor measurement units (PMUs) are used for the collection of data in real time for the smart grid energy systems. Intrusion detection and cyber security of network are important requirements for maintaining the integrity of wide area monitoring systems. The intrusion detection methods analyze the measurement data to detect any possible cyber attacks on the operation of smart grid systems. In this paper, the model-based and signal-based intrusion detection methods are investigated to detect the presence of malicious data. The chi-square test and discrete wavelet transform (DWT) have been used for anomaly-based detection. The false data injection attack (FDIA) can be detected using measurement residual. If the measurement residual is larger than expected detection threshold, then an alarm is triggered and bad data can be identified. Avoiding such alarms in the residual test is referred to as stealth attack. There are two protection strategies for stealth attack: (1) Select a subset of meters to be protected from the attacker (2) Place secure phasor measurement units in the power grid. An IEEE 14-bus system is simulated using real time digital simulator (RTDS) hardware platform for implementing attack and detection schemes.