An Exploratory Investigation on Modelling Technologies to Flexible Loads Dispatching in A Smart Grid Environment

There are a variety of flexible energy demand loads in modern buildings. In particular, heating, ventilation and air conditioning (HVAC) system accounts for around 50% of buildings' total energy consumption and is a major source for energy scheduling flexibility. Electric vehicle (EV) charging is an emerging load in many buildings, and may also provides scheduling flexibility depending on the charging requirements. In addition, the usage of energy storage systems such as batteries further increases the flexibility in scheduling building energy demands. At the power grid level, it is important to leverage such flexibility for improving energy efficiency. To this end, we have proposed a proactive demand response scheme that enables building customers to actively participate in the electricity market clearing process. Compared to the conventional passive demand response strategy, the proactive scheme is shown to be more effective to take advantage of buildings' flexible load demand and enhance the energy efficiency of the overall power system. In this paper, the scheduling of various flexible building energy loads such as HVAC control and EV charging, as well as the usage of battery storage system, are jointly modeled and optimized in the proactive demand participation framework. We conduct experiments on the IEEE 5-bus system to evaluate the impact of heterogeneous flexible loads on the energy efficiency of the proactive scheme.

With the progress of technology and economic development, power systems are becoming more and more complex, and distributed generation is becoming more and more popular. This paper proposes a risk assessment system for smart grid dispatching based on flexible load considerations. The model includes flexible control, fuzzy comprehensive evaluation and uncertainty analysis. The piecewise linear programming method is used to establish nonlinear equations to obtain the optimal solution when making decisions in the distribution network. At the same time, the simulation results calculated by MATLAB software show that the simulation results of flexible load participating in distribution network regulation and control are compared with the simulation results of flexible load participating in distribution network regulation and control, It can be seen from the power consumption planning diagram of flexible load 1 to flexible load 5 that the power consumption of flexible load is basically operated according to the planned power consumption. From the overall load planning and dispatching results, the flexible load can better track the actual load curve.

Integrating renewable energy into power grids is seen in increase in recent years since these energy sources are sustainable and clean. However, the integration brings about considerable technical challenges associated with fluctuations and uncertainties of the energy availability whilst maintaining the stability of smart grids. The prediction of renewable energy generation is key to achieve optimal power dispatch in renewable-intensive smart grids. However, uncertain interruption and prediction errors will make an optimal decision more challenging. Model predictive control (MPC) is an effective way to overcome the discrepancies between the prediction and the real-world system through a closed-loop correction over iteration process. This study develops an improved MPC scheme used with a hybrid energy storage system for optimal power dispatch in a smart grid. This hybrid renewable energy system consists of a wind farm, a hydrogen/oxygen storage system and several fuel cells (FCs). In this study, particle swarm optimization (PSO) with a back propagation (BP) artificial neural network is developed to predict the wind energy availability by using measured data. Then, a genetic algorithm (GA) is combined with a state space model (SSM) to achieve the MPC control. A dataset of 24-hour ahead predictive generation is calibrated from the measured data and is defined for optimal power flow between the grid, the wind farm and the storage subsystem so as to balance the supply and load. The optimization target is to achieve a minimal energy exchange between the power grid and the hybrid renewable energy storage system. Based on actual measured data, the test results have shown that the proposed methodology can maximize the local usage of wind power whilst minimizing the power exchange with the grid. An optimal power dispatch strategy is proved to be effective to meet the demand and efficiency with dynamic control of the FCs. The usage of the intermittent wind power is increased from 45% to 90% in the four test studies. Therefore, this work can minimize the impact of fluctuating renewable energy on the power grid and enhance uptakes of FC-based energy systems. This is particularly economic and relevant to the remote and under-developed regions where their power networks are weak and vulnerable.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

The Economic Dispatch Problem (EDP) plays a critical role in power system operations by trying to allocate power generation across multiple units at minimal cost while satisfying complex operational constraints. Traditional optimization techniques struggle with the non-convexities introduced by factors such as valve-point effects, prohibited operating zones, and spinning reserve requirements. While metaheuristics methods have shown promise, they often suffer from convergence issues and constraint-handling limitations. In this study, we introduce a novel application of Group Relative Policy Optimization (GRPO), a reinforcement learning framework that extends Proximal Policy Optimization by integrating group-based learning and relative performance assessments. The proposed GRPO approach incorporates smart initialization, adaptive exploration, and elite-guided updates tailored to the EDP’s structure. Our method consistently produces high-quality, feasible solutions with faster convergence compared to state-of-the-art metaheuristics and learning-based methods. For instance, in the case of the 15-unit system, GRPO achieved the best cost of USD 32,421.67/h with full constraint satisfaction in just 4.24 s, surpassing many previous solutions. The algorithm also demonstrates excellent scalability, generalizability, and stability across larger-scale systems without requiring parameter retuning. These results highlight GRPO’s potential as a robust and efficient tool for real-time energy scheduling in smart grid environments.

A smart grid is an intelligent power grid. In recent years, the smart grid environment and its applications are incorporated into a variety of areas. The smart grid environment, however, can expose much more information than the old environments. In the environment, smart devices can be media in the exposure of various and specific pieces of information as well as energy consumption. This poses a huge risk in that it, combined with other pieces of information, may expose much more information. The current smart grid environment raises a need to develop anonymous signature and authentication techniques to prevent privacy breaches. Trying to meet this need, the principal investigator conducted research for three years. This paper discusses both the research trends investigated by him and the limitations of the development research and future research in need. Smart grid security requires the development of encrypted anonymous authentication that is applicable to power plant security, including nuclear power plants as well as expandable test beds.

With numerous diverse flexible loads widely connected to power grid, the heterogeneity of flexible loads has become difficult to adapt to the large-scale power balance demand of the power grid. Optimal configuration of energy storage considering the heterogeneous flexible loads is the key to realize the power balance of the regional power grid. Firstly, based on the differentiated characteristics and power consumption curve of heterogeneous loads, the heterogeneous loads are classified as transferable loads, cuttable loads and adjustable loads. Then, the coordinated optimization strategy is proposed for heterogeneous loads and multi-type energy storages. the optimal configuration of multi-type energy storages is achieved by mixed integer linear programming (MILP). Finally, compared to flexible loads or energy storages participating in grid regulation alone, the proposed optimization strategy can not only achieve power balance of the regional grid, but also reduce the system operation cost and improve the economic benefits of the grid.

The energy storage capabilities of the heating, ventilation, and air conditioning (HVACs) systems, as well as electric vehicles (EVs), render them excellent candidates for demand response. This study proposes a joint coordination of AC optimal power flow and demand response for aggregated EVs and HVACs, in conjunction with the scheduling of large-scale flexible loads. This paper prioritizes user convenience, and ensuring compliance with comfortable temperature boundaries. Additionally, EV capacity and travel distance are estimated using a normal distribution. The primary objective of this study is to minimize the total cost and peak demand while simultaneously maximizing total revenue of participant user in demand response, user convenience, and the integration of renewable energy sources. To achieve an accurate optimal choice, the uncertainties associated with renewables and demand are addressed using scenario-based and long short-term memory (LSTM) methods, respectively. The optimal demand response level is determined through sensitivity analysis, and employing the technique for order preference by similarity to ideal solution (TOPSIS) method. The obtained results demonstrate that by only 20% demand response at large-scale loads and EV and HVAC aggregators, the proposed model reduces peak loads by 12.8% and the total cost by 7.4%.

This paper considers the optimal economic dispatch of power generators in a smart electric grid for allocating power between generators to meet load requirements at a minimum total cost. We present a decentralized algorithm where, each generator independently adjusts its power output using only a measurement of the frequency deviation of the grid and minimal information exchange with its neighbors. Existing algorithms assume that frequency deviation is proportional to the load imbalance. In practice this is seldom exactly correct. We assume here that the only thing known about this relationship is that it is an unknown, odd, strictly increasing function. We provide a proof of convergence and simulations verifying the efficacy of the algorithm.

Designing an efficient distributed economic dispatch (DED) strategy for the smart grid (SG) in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power system, such as easy implementation, low maintenance cost, high energy efficiency, and strong robustness against uncertainties. It has drawn a lot of interest from a wide variety of scientific disciplines, including power engineering, control theory, and applied mathematics. We present a state-of-the-art review of some theoretical advances toward DED in the SG, with a focus on the literature published since 2015. We systematically review the recent results on this topic and subsequently categorize them into distributed discrete- and continuous-time economic dispatches of the SG in the presence of multiple generators. After reviewing the literature, we briefly present some future research directions in DED for the SG, including the distributed security economic dispatch of the SG, distributed fast economic dispatch in the SG with practical constraints, efficient initialization-free DED in the SG, DED in the SG in the presence of smart energy storage batteries and flexible loads, and DED in the SG with artificial intelligence technologies.

A new model of power system to identify critical elements for smart and bulk power transmission grid environment is presented in this paper. Proposed bidirectional power flow model under complex network framework can quantify the criticality of nodes whose removal would trigger cascading failure. The proposed model based centrality, which is an extension of nominal power flow based model, is able to find nodes whose removal exerts more stress on the system. Several measures of impact like path length increase, boosted connectivity loss, raised load loss and increase in number of overloaded lines justify the use of bidirectional power flow based model to evaluate the criticality in smart grid environment. Change in direction of power flow in various random lines of the test system has a very little effect on the rank of nodes in the critical list.

MQRP: Mobile sinks-based QoS-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things

High-data-rate mobile multimedia applications can greatly increase energy consumption, leading to an emerging trend of addressing the “energy efficiency” aspect of mobile networks. Cognitive mobile networks with small cells are important techniques for meeting the high-data-rate requirements and improving the energy efficiency of mobile multimedia communications. However, most existing works do not consider the power grid, which provides electricity to mobile networks. Currently, the power grid is experiencing a significant shift from the traditional grid to the smart grid. In the smart grid environment, only considering energy efficiency may not be sufficient since the dynamics of the smart grid will have significant impacts on mobile networks. In this paper, we study green cognitive mobile networks with small cells in the smart grid environment. Unlike most existing studies on cognitive networks, where only the radio spectrum is sensed, our cognitive networks sense not only the radio spectrum environment but also the smart grid environment, based on which power allocation and interference management for multimedia communications are performed. We formulate the problems of electricity price decision, energy-efficient power allocation, and interference management as a three-stage Stackelberg game. A homogeneous Bertrand game with asymmetric costs is used to model price decisions made by the electricity retailers. A backward induction method is used to analyze the proposed Stackelberg game. Simulation results show that our proposed scheme can significantly reduce operational expenditure and CO2 emissions in cognitive mobile networks with small cells for multimedia communications.

UHV AC and DC and new energy (wind power, photovoltaic) are developing rapidly on a large scale, and the contradiction between the scheduling space of traditional power grid regulation resources and the scale of intermittent new energy is prominent. This paper comprehensively considers various control resources including conventional power supply, flexible load, energy storage, DC cross-zone, district tie line, and load transfer, and designs a coordinated dispatching framework for source-grid-load-storage to carry out research on key technologies for coordinated and optimized dispatching of source-grid-load-storage in day-ahead, intra-day, real-time, and emergency multi-time scale. Case studies show that making full use of load-side resource demand elasticity can solve the problem of insufficient peak power supply capacity of the grid.

Cyber physical systems (CPSs) utilised in many of the critical infrastructures including the smart grid. The operational activities of smart grid's CPS use different industrial solution such as industrial control systems (ICS) for monitoring and controlling the physical architecture of smart grid. Therefore, it is quite essential to keep these industrial control systems secure and safe from potential hazards and cyber world as it may be harmful for human safety, strategic infrastructure of a country and also for the economy. The study gathered the scattered guidelines, best practices and policies available from various organisations such as National Institute of Standards and Technology (NIST), ISO, and North American Electric Reliability Corporation (NERC) for the purpose of developing safe and secure ICS security systems and also the knowledge about the issues involved in ICS security and protection. A conceptual framework is proposed and developed to address the shortcomings in-order to protect the ICS systems in a smart grid environment. The proposed framework was developed by reviewing related studies, standards and utilising IT security working experience. This framework will not only expand the horizon of knowledge but also provides pathway for the development of existing activities of CPS applications such as smart grid.