Optimal Energy Allocation Research Articles

Optimal noise injection energy allocation and collection of energy harvesting attacker under quality-changeable environment

Optimal noise injection energy allocation and collection of energy harvesting attacker under quality-changeable environment

Estimation of Regional Electricity Consumption Using National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite Night-Time Light Data with Gradient Boosting Regression Trees

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

An optimal scheduling strategy for electricity-thermal synergy and complementarity among multi-microgrid based on cooperative games

Multi-microgrid (MMG) systems provide an effective way to convert renewable energy into other forms of energy for low-carbon utilization. However, the coupling of multi-energy and renewable energy brings a challenge to the stable operation and dispatch of the system. Therefore, an electricity-thermal-gas-hydrogen MMG complementary optimization model based on peer-to-peer (P2P) electricity-thermal synergy is proposed. The model focuses on the coupling and conversion of electricity, hydrogen, and thermal through a hydrogen energy storage system (HESS) combined with renewable energy, and incorporates integrated demand response (IDR) and ladder-type carbon trading mechanism (LCTM). Subsequently, based on the cooperative game and the alternating direction multiplier method (ADMM), a multi-energy synergistic and complementary optimal scheduling strategy is proposed, which increases the flexibility of multi-energy sharing. In addition, a revenue allocation optimization strategy that takes into account the fairness and renewable energy consumption rate is proposed to achieve the stability of the alliance system operation. Finally, the simulation results show the effectiveness of the proposed model and strategy, realizing the complementary sharing of regional multi-energy, enabling the expansion of the boundaries of optimal energy allocation, further improving the low-carbon economic operation capability of MMG, and reducing the dependence of the system on the energy market.

Multi-Objective energy management of Solar-Powered integrated energy system under forecast uncertainty based on a novel Dual-Layer correction framework

Integrated energy systems (IESs) are increasingly pivotal in the global shift towards sustainable energy frameworks. Within IESs, the energy management system (EMS) plays a critical role, tasked with optimizing energy allocation to achieve objectives like grid stability, energy reliability, and cost-efficiency. A significant challenge for EMS is the inherent unpredictability of renewable energy. Given this, a model predictive control (MPC)-based real-time energy management framework is proposed, which aims to mitigate the impacts of radiation forecast uncertainties in solar-powered IES. A dual-layer correction mechanism is proposed to quantify forecast uncertainty, resulting in uncertain intervals inferred from the hidden Markov model (HMM). Then, the uncertain intervals are adeptly managed within the MPC decision-making framework through the constraint-tightening method. A case study on solar-powered electricity, heating, and hydrogen IES demonstrates the validity of the proposed framework. Five different sets of energy management strategies, such as deterministic analysis, distributionally robust optimization, and the proposed HMM-MPC are subjected to long-term simulations. Comparative analysis reveals that the proposed method enhances various aspects of the performance metrics, including demand response, energy reliability, and system self-sufficiency.

Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle

In order to attain the optimal allocation of energy between auxiliary power unit (APU) and battery of the connected automated range-extended electric vehicle, from a multi-scale perspective, an Approximate Optimal Energy Management Strategy (AOEMS) has been proposed. Firstly, a composite determination method was designed based on prediction and parameter identification to divide the operating condition types, which could be as an operating condition prediction and feed forward control method to determine the approximate optimal power distribution between APU and battery. This method keeps APU operating at the optimal operating point/area as much as possible without predicting the accurate vehicle speed sequence. Then, an adaptive method based on V2X information was used to adjust the key threshold parameters in real-time using a fuzzy logic controller which reduces the algorithm complexity with prediction and division of the operating conditions types, which has significantly improved the robustness and overall performance of the optimization. Finally, the simulation and experimental results thoroughly indicated that the proposed AOEMS can better balance the performances, as anticipated, enhancing economy, reducing emissions, and extending battery lifewere effectively maintained in equilibrium.

A transferable perception-guided EMS for series hybrid electric unmanned tracked vehicles

This work investigates the optimal energy allocation considering the different road properties for a series hybrid electric unmanned tracked vehicle. Tracked vehicles operate mostly in off-road conditions, where the energy consumption changes heavily due to the road smoothness. However, few works considered the effect of explicit road properties on energy allocation for tracked vehicles. Besides, conventional energy management strategies are generally difficult to adapt to the fast-changing off-road conditions. To address these challenges, a perception-guided energy management strategy based on deep reinforcement learning that takes road roughness as explicit features into account is proposed. A method of road roughness extraction and quantification is proposed based on the random sample consensus algorithm and singular value decomposition. To enhance the deployment efficiency in different off-road driving conditions, a deep transfer learning framework of the proposed perception-guided energy management strategy is devised. Experimental results demonstrate that the perception-guided energy management strategy improved the fuel economy by 8.15 %. Moreover, the transferable energy management strategy achieves a convergence rate of 34.15 % better than the relearned energy management strategy. Our code is available at https://github.com/BIT-XJY/PgEMS.

Low-carbon economic dispatch strategy for microgrids considering stepwise carbon trading and generalized energy storage

Integrating carbon trading mechanisms with generalized energy storage (GES) fully embodies the principles of green and coordinated development, serving as a crucial means to achieve low-carbon construction of microgrids. This research presents a strategy for optimizing energy allocation within microgrids to minimize carbon emissions and enhance microgrid systems' economic-environmental benefits. The strategy takes into account the use of tiered carbon trading and GES. Based on a typical microgrid system architecture, an economic dispatch model for microgrids is developed, which integrates renewable energy sources such as wind and solar storage, gas turbines, energy storage systems, and flexible resources on the demand side. The model aims to minimize carbon emissions while optimizing the allocation of resources. Subsequently, the model facilitates microgrid carbon emission control by considering the transferable, convertible, and reducible properties of GES. Furthermore, implementing a tiered carbon trading mechanism decreases carbon emissions. Finally, using a real microgrid example from a specific region in China, the results indicate that the proposed method significantly enhances the system's low-carbon level. Notably, compared to scenarios that do not consider GES, the proposed method substantially reduces total costs by 6.62% and decreases carbon emissions by 22.2%. The findings indicate that the suggested dispatch model can substantially decrease carbon emissions while simultaneously improving the economic efficiency of the microgrid system.

Benefits and costs: Understanding the influence of heavy metal pollution on environmental adaptability in Strauchbufo raddei tadpoles through an energy budget perspective

Understanding the impact of environmental pollution on organismal energy budgets is crucial for predicting adaptive responses and potential maladaptation to stressors. However, the regulatory mechanism governing the trade-off between energy intake and consumption remains largely unknown, particularly considering the diverse adaptations influenced by exposure history in realistic field conditions. In the present study, we conducted a simulated field reciprocal transplant experiment to compare the energy budget strategies of Strauchbufo raddei tadpoles exposed to heavy metal. The simulated heavy metal concentrations (0.29 mg/L Cu, 1.17 mg/L Zn, 0.47 mg/L Pb, 0.16 mg/L Cd) mirrored the actual environmental exposure concentrations observed in the field habitat. This allowed for a comparison between tadpoles with parental chronic exposure to heavy metal pollutants in their habitat and those without such exposure. Results revealed that under heavy metal exposure, tadpoles originating from unpolluted areas exhibited heightened vulnerability, characterized by reduced food intake, diminished nutrient absorption, increased metabolism cost, reduced energy reserves, and increased mortality rates. In contrast, tadpoles originating from areas with long-term heavy metal pollution demonstrated adaptive strategies, manifested through adjustments in liver and small intestine phenotypes, optimizing energy allocation, and reducing energy consumption to preserve energy, thus sustaining survival. However, tadpoles from polluted areas exhibited certain maladaptive such as growth inhibition, metabolic suppression, and immune compromise due to heavy metal exposure. In conclusion, while conserving energy consumption has proven to be an effective way to deal with long-term heavy metal stress, it poses a threat to individual survival and population development in the long run.

Optimal design and allocation of stealthy attacks against remote state estimation for cyber-physical systems

This paper studies the design and allocation problem for stealthy attacks against remote state estimation for cyber-physical systems, where the data are transmitted through multiple wireless communication channels. With limited budget for the available energy at each step and total energy over the finite-time horizon, the aim of the stealthy attacker is to maximize the estimation error based on the collaboration between the attack matrices and schedule, which is formulated as the optimization problem with multi-variable coupling. Under the presented framework of stepwise optimization, the optimization problem is separated into two subproblems with the guarantee of optimality. First, for the optimal design of attack matrices, the analytical expression is derived by employing the proposed data isolation technique instead of the assumption utilized in the existing results that the output matrix is of full row rank. And then, for the optimal allocation of energy constrained attacks, the scheduling sequence is obtained by solving the transformed linear 0-1 programming. Finally, simulation results sustain the performance of the presented attack strategy.

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

This paper introduces a groundbreaking approach to energy management in smart cities, leveraging deep reinforcement learning (DRL) and secured blockchain technology. The escalating demand for energy in urban areas necessitates intelligent solutions that optimize energy usage while ensuring data security and integrity. Our proposed technique integrates DRL algorithms with blockchain technology to achieve optimal energy management while addressing security concerns. The core innovation lies in the development of a DRL framework that dynamically adapts to changing energy demands and environmental factors. By continuously learning and optimizing energy allocation strategies, our approach enhances resource utilization efficiency and reduces wastage, contributing to sustainable smart city initiatives. Additionally, the incorporation of blockchain technology ensures data immutability, transparency, and secure transactions within the energy management system. We validate the effectiveness of our method through extensive simulations and real-world experiments, demonstrating significant improvements in energy efficiency, cost savings, and security compared to traditional approaches. Our research paves the way for future advancements in smart city infrastructure, offering a scalable and robust solution for optimal energy management with heightened security measures.

Lightweight three-party key agreement for V2G networks with physical unclonable function

Electric vehicles (EV) are gradually replacing traditional fuel-based vehicles to reduce carbon emissions and protect the human living environment. EVs can be easily integrated into the smart grid infrastructure, and achieve the rapid demand response of power exchange in the vehicle-to-grid (V2G) ecosystem, and achieve optimal energy allocation. However, it is a challenge to simultaneously solve the security, privacy and efficiency of communication in V2G. To meet this challenge, we propose a three-party key agreement protocol based on Physical Unclonable Function (PUF). The proposed protocol just uses a small amount of XOR operations, PUF functions, and hash functions with low computation cost. It is a truly lightweight and efficient three-party key agreement protocol. It realizes the privacy protection of one-time random pseudonym under the condition that the server only needs to store two pseudonyms. And it also provides a supplementary sub-protocol for pseudonym error conditions to achieve high robustness of the authentication system. Strict security formal proof shows the proposed protocol meets various security requirements. Compared with similar protocol, the proposed protocol reduces the total communication cost by 73% and the computing cost by 79%.

Allocation of Eavesdropping Attacks for Multi-System Remote State Estimation

In recent years, the problem of cyber–physical systems’ remote state estimations under eavesdropping attacks have been a source of concern. Aiming at the existence of eavesdroppers in multi-system CPSs, the optimal attack energy allocation problem based on a SINR (signal-to-noise ratio) remote state estimation is studied. Assume that there are N sensors, and these sensors use a shared wireless communication channel to send their state measurements to the remote estimator. Due to the limited power, eavesdroppers can only attack M channels out of N channels at most. Our goal is to use the Markov decision processes (MDP) method to maximize the eavesdropper’s state estimation error, so as to determine the eavesdropper’s optimal attack allocation. We propose a backward induction algorithm which uses MDP to obtain the optimal attack energy allocation strategy. Compared with the traditional induction algorithm, this algorithm has lower computational cost. Finally, the numerical simulation results verify the correctness of the theoretical analysis.

Leveraging Deep Q-Learning to maximize consumer quality of experience in smart grid

The smart grid system has addressed the problems of traditional power grid by not only meeting energy demand in real-time but also limiting its wastage. The two key objectives of a smart grid system are to ensure a higher Quality of Experience (QoE) for consumers and to reduce consumer costs using dynamically varying pricing concepts. However, these two objective parameters oppose each other as maximizing the consumer QoE requires the availability of sufficient electric power at any given time, which in turn increases power purchase cost. In this paper, we introduce an efficient power management system architecture of a smart grid and develop an Optimal Energy Allocation and Prediction system based on Deep Q-Leaning, namely OEAP-DQL, that brings a trade-off between the two. The developed OEAP-DQL system is a multi-objective linear programming (MOLP) problem that predicts consumer electricity demand by exploiting four different weighted and regressive moving average forecasting methods in the action space to accurately capture dynamically varying customer demand behaviors. Furthermore, iterative exploitation of multiple learning methods decreases forecasting error and intelligent stored power management helps the OEAP-DQL smart grid operator (SGO) to enhance its profit. The results of our simulation experiments show that the OEAP-DQL system outperforms the state-of-the-art works in terms of QoE and cost.

Optimal Energy Allocation in a Car Park for Electric Vehicles

Optimal Energy Allocation in a Car Park for Electric Vehicles

Collaborative Optimization Decision Support System for Biomass and Smart Energy Based on Virtual Power Plant

Abstract Energy is an important basic resource for the development of human society. This paper develops a decision support system for collaborative optimization of biomass energy and intelligence using biomass energy and smart energy management technology. Explain the various methods by which biomass can be transformed into usable energy for power plants. A smart energy management model is constructed using Internet of Things technology, and its performance prediction model is predicted using the gray prediction model. To study the optimal allocation of energy in the system constructed in this paper, the objective function and constraints are built using carbon capture technology, and the optimal allocation conditions of energy are calculated. After experimental analysis, it was found that in 2020, biomass energy power generation reached 29 million kilowatts, which was 020.1 million kilowatts higher than in 2015. The co-optimization strategy of biomass energy and smart energy constructed in this paper supports the energy utilization efficiency of the system to reach more than 60%. Compared to the traditional energy division system, the constructed system saves an annual cost of 52.71%. The payback period was shortened to 3.31 years, resulting in a net present value increase of 2.8991 million yuan. The energy synergy system’s power supply produces the least amount of carbon dioxide from an environmental perspective and has been stabilized at 50 kg.

Optimal energy allocation based on SINR under DoS attack

According to signal to interference plus noise ratio (SINR) of channel, we consider optimal attack schedule against remote state estimation in cyber–physical systems (CPSs) under denial of service (DoS) attack. Each physical process has a smart sensor that acquires the measurement of the system and transmits its local state estimation to the remote estimation terminal through a wireless channel. At each time step, a DoS attacker can send a jamming attack to one of the two channels. On the side of the attacker, this paper aims to search an optimal strategy, which maximizes the estimation error of the remote estimator. We propose an algorithm to obtain the optimal attack schedule policy by using of Markov decision process (MDP). A numerical example is provided to demonstrate the effectiveness of the proposed methods.

Optimal Attack Energy Allocation Against Remote Estimation in Cyber-Physical Systems

This paper focuses on designing an optimal attack energy allocation strategy to disrupt the performance of remote state estimation with unknown gains for cyber-physical systems (CPSs). To deal with the unknown gains, the Cramér-Rao Bound (CRB) is introduced to obtain a lower bound of the secondary moment of the estimate error. Then, by solving an optimization problem that maximizes the average CRB over a finite time horizon with energy constraint, an optimal attack energy allocation strategy is derived. Compared with the existing works, the rigorous assumption that the attacker has full knowledge of the system is removed, and the computational complexity of solving the optimization problem is significantly reduced. Finally, numerical results validate the presented strategy.

A fair and effective approach to managing distributed energy resources through peer-to-peer energy trading with load prioritization among smart homes

This paper proposes a novel approach to effectively manage distributed energy resources (DERs) by facilitating peer-to-peer (P2P) energy trading among smart homes. This system incentivizes peers to trade surplus energy, thereby maximizing social welfare. The proposed day-ahead demand-side management (DSM) technique considers load prioritization based on individual user needs. It employs an optimal energy allocation policy that encourages peers to purchase energy at reduced rates while selling it at advantageous prices. This strategy optimizes the allocation of traded energy to higher-priority loads, followed by lower-priority ones, all while considering user preferences. The proposed method results in an average reduction in energy costs from 14.80 % to 2.70 % for all consumers and an average increase in revenues from 17.35 % to 9.07 % for all prosumers compared to the grid and previously proposed studies. Moreover, the proposed approach significantly enhances the consumer satisfaction Index (SI), with a 46.8 % increase over the previous research study. Simulation results demonstrate substantial economic benefits for all market participants, improved SI, enhanced community self-sufficiency, and reduced dependence on the main grid. The proposed methodology offers a practical and fair solution for managing DERs through P2P energy trading by implementing load prioritization within smart homes. It highlights the potential to establish a sustainable and efficient energy ecosystem, thereby enhancing the effective utilization of surplus energy from DERs.

Optimal Configuration of Power/Thermal Energy Storage for a Park-Integrated Energy System Considering Flexible Load

The park-integrated energy system can achieve the optimal allocation, dispatch, and management of energy by integrating various energy resources and intelligent control and monitoring. Flexible load participation in scheduling can reduce peak and valley load, optimize load curves, further improve energy utilization efficiency, and reduce system costs. Based on this, firstly, a flexible power-load model is established considering the translatable load, transferable load, and reducible load; and a thermal flexible load model is established based on the fuzziness of user perception of temperature in this study; then, the mixed integer linear programming method is adopted, and the sum of the carbon transaction cost, operation and maintenance cost, compensation cost, power purchase cost, gas purchase cost, wind and light abandonment penalty cost and investment cost of the system is minimized as the objective function, and the configuration of the integrated energy system is optimized, and the optimal capacity of each equipment and the output of each period are obtained. Finally, taking an industrial park in Liaoning Province of China as an example, the analysis is carried out. The example results show that by scheduling the flexible electrical load and flexibly adjusting the indoor temperature, renewable energy consumption can be promoted, and electricity load and heat-load curves can be optimized to increase the installed capacity of wind turbines, reduce the capacity of gas turbines, batteries, and heat-storage tanks, improve system economy, and improve the penetration rate of renewable energy.