Local Energy Consumption Research Articles

Secure Wireless Sensor Networks: A Weighted K-NN and RNN-based Approach for Attack Detection and Localization

In the realm of wireless sensor networks (WSNs), precise localization of sensor nodes and attack detection has emerged as a significant challenge. Accurate localization of sensor nodes and detection of attacks is crucial for enhancing the network lifetime and preventing the data from unauthorized access. However, prior research has encountered various obstacles, such as high localization error, more time consumption, and poor detection performance. To overcome these difficulties, this research proposes a novel Recurrent Crossover-based Dynamic Differential (RC-DD) algorithm for effective localization and attack detection in WSN. The proposed model utilizes a weighted K-Nearest Neighbor (KNN) with Mahalanobis distance for accurately localizing the position of the sensor nodes within the network. Moreover, the Recurrent Neural Network (RNN) is employed for effectively detect multiple attacks, and Dynamic Differential Annealed Optimization (DDAO) with crossover strategy is implemented for fine-tuning the RNN’s hyperparameter, thereby enhancing the attack detection accuracy. The experimental outcome shows that the proposed RC-DD framework effectively localizes the sensor nodes and detects the attacks with a higher accuracy of 98.9% compared to existing methodologies. Additionally, the proposed framework minimizes localization error and energy consumption, underscoring its potential to enhance the security and lifetime of the network.

A digital twin of multiple energy hub systems with peer-to-peer energy sharing

As climate change has become a global concern, the decarbonization of energy systems has become a prominent solution for CO2 emission reduction. The recent emergence of multi-energy hub systems (MEHs), characterized by interconnected energy hubs (EHs) and facilitated by energy sharing, presents a promising solution for seamlessly integrating a significant share of renewable energy sources (RESs) and flexibility among EHs. Faced with the intricate interplay and uncertainty of future energy markets, an extensive digital twin (EXDT) is proposed to perform predictive testing and evaluate the performance of MESs. This EXDT provides energy system operators with insights into the coordinated behavior of interconnected EHs under various future scenarios, thus contributing to smarter decision-making processes. Specifically, an array of scenarios including different decision-making strategies and P2P energy sharing strategies were considered. For each of these scenarios,"what-if" tests were conducted using a multi-agent reinforcement learning (MARL)-based method to model the stochastic decision-making process of EHs belonging to different stakeholders with access to local information. Uncertainties during operation can be mitigated using Markov Game (MG) by capturing knowledge from historical energy data. Subsequently, the economic and technical performance were evaluated using multidimensional evaluation indexes. The proposed MARL-based EXDT was applied to a representative 4-EH multi-energy system in China. Simulation results indicate that P2P energy sharing facilitates the local consumption of renewable energy, providing additional financial benefits and self-sufficiency to each EH and offering peak shaving to the upstream grid. Additionally, system performance under various decision-making and P2P sharing strategies was tested and evaluated to identify the impact of these strategies on system operation.

Robot path planning based on IAPF-A-Star algorithm

The traditional A-Star algorithm has the problem of performing much invalid traversal work of nodes near concave obstacles when dealing with path planning tasks in the presence of concave obstacles. To solve this problem, a fusion algorithm (called IAPF-A-Star here) based on improved artificial potential field (IAPF) and A-Star is proposed. In IAPF-A-Star, to better measure the distances between the nodes and the target node, the potential energy of the artificial potential field is used to represent the node’s proximity to the target node, and an iterative correction method is used to improve the potential field in order to eliminate the local low-potential energy that may exist in the artificial potential field. The improved potential field effectively avoids the occurrence of local low-potential fields and makes it possible to measure the position of nodes in relation to their target node by means of the potential field. Finally, the obtained path is converted to a curve path by using a cubic spline curve. In addition, to solve the problem that the curve path generated by the cubic spline curve may collide with obstacles, a path optimization strategy based on an adaptive cubic spline curve is proposed to make the final path smooth and collision avoidance, shorter and closer to the actual situation. Simulation experiments show that the proposed IAPF-A-Star algorithm in this paper can effectively avoid the problem of additional local energy consumption caused by concave obstacles when processing path planning tasks, and the traversal of invalid nodes facing different complex concave obstacles is effectively avoided.

Evaluation of a Peer-To-Peer Smart Grid Using Digital Twins: A Case Study of a Remote European Island

Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

Local Energy Community as a Small Hydrogen Valley – H2LEC

A Local energy community (LEC) is a vertically nested system in energy supply and an ecosystem with joint values and objectives. On-site integrated hydrogen-based systems connected to the grid, and consisting of electrolyser, hydrogen storage and fuel cell system - hydrogen prosumers - provide efficient balancing of local energy consumption and local production of renewable energy that can be extended over annual cycles. There is no transport of hydrogen needed. Thus, the H2LEC – Local energy community with integrated hydrogen systems - represents the carrier of dispersed energy and hydrogen production as a complement of concentrated energy and hydrogen production: on average, H2LEC will predictably achieve at least 75% self-supply. Additionally, with Combined Heat-and-Power systems, coupling to the thermal system adds to energy efficiency. H2LEC represents a virtual socio-economic system based on community values and thus engages initiative, innovation and capital of local actors, new technology start-ups and local industry; and represents opportunities for new disruptive business models. It brings into the energy supply system new players – prosumer who actively trade their flexibilities among themselves and on the external markets, and stimulates new enabling technologies - notably automated close-to-real time trading - thereby boosting end-to-end automated solutions. The H2LEC create the need and the market for smaller integrated hydrogen-based systems ranging from a few kWe for residential homes to a few MWe units for larger industrial companies or local districts with a complete range of capacities in between, for public or tertiary buildings and smaller enterprises. Thus, they provide an opening for participation of SMEs in local and international value chains and will create a strong complementary energy bottom-up pillar and hydrogen supply system locally and in Europe.

Parametric Dueling DQN- and DDPG-Based Approach for Optimal Operation of Microgrids

This study is aimed at addressing the problem of optimizing microgrid operations to improve local renewable energy consumption and ensure the stability of multi-energy systems. Microgrids are localized power systems that integrate distributed energy sources, storage, and controllable loads to enhance energy efficiency and reliability. The proposed approach introduces a novel microgrid optimization method that leverages the parameterized Dueling Deep Q-Network (Dueling DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms. The method employs a parametric hybrid action-space reinforcement learning technique, where the DDPG is utilized to convert discrete actions into continuous action values corresponding to each discrete action, while the Dueling DQN uses the current observation states and these continuous action values to predict the discrete actions that maximize Q-values. This integrated strategy is designed to tackle the co-scheduling challenge in microgrids, enabling them to dynamically select the most favorable control strategies based on their specific states and the actions of other intelligent entities. The ultimate objective is to minimize the overall operational costs of microgrids while ensuring the efficient local consumption of renewable energy and maintaining the stability of multi-energy systems. Simulation experiments were conducted to validate the efficacy and superiority of the proposed method in achieving the optimal microgrid operation, showcasing its potential to improve service quality and reduce operational expenses. Average rewards increased by 30% and 15% compared to the use of the Dueling DQN or DDPG only.

Comparison of energy consumption prediction models for air conditioning at different time scales for large public buildings

Large public buildings have complex functions and high air conditioning energy consumption, and the prediction of energy consumption for air conditioning in different time scales can realise different energy saving management needs. Taking a large public building in Guangzhou as a research case, the long and short-term memory neural network (LSTM) is used as the basis to establish the machine learning model for energy consumption prediction for air conditioning at different time scales in terms of 10 min, hourly and daily, and a comparative analysis is carried out. The original data were analysed and processed by Z-Score and Local Outlier Factor (LOF) outlier detection methods. The relationship between meteorological parameters for Typical Meteorological Year (TMY) and the actual local meteorological parameters and energy consumption for air conditioning was analysed using linear regression, Pearson and Spearman correlation coefficients, taking meteorological parameters as input variables. The results show that comparing energy consumption for air conditioning with the meteorological parameters in TMY and actual local meteorological parameters, the actual meteorological parameters had a stronger correlation energy consumption for air conditioning. When the actual local meteorological parameters are taken as input variables, the mean absolute percentage error (MAPE) is reduced by about 0.85 %, and the coefficient of determination (R2) is increased by about 0.01. At the same time, the MAPE and R2 of the 10-min energy consumption prediction model are about 6.43 % and 0.9738, respectively, the MAPE and R2 of the hourly prediction model are about 9.29 % and 0.9568, respectively, while the MAPE and R2 of the daily prediction model are about 25.76 % and 0.7998. Meanwhile, an improved energy consumption prediction model is proposed to reduce the daily energy consumption prediction error to 6.36 % for MAPE, and the R2 of this model is 0.9927.

Deep reinforcement learning based resource provisioning for federated edge learning

With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks simultaneously, and since UEs can have multiple data sources that are suitable for various FL tasks, multiple tasks FL could be a promising way to respond to different application requests at the same time. However, running multiple FL tasks simultaneously could lead to a strain on the device’s computation resource and excessive energy consumption, especially the issue of energy consumption challenge. Due to factors such as limited battery capacity and device heterogeneity, UE may fail to efficiently complete the local training task, and some of them may become stragglers with high-quality data. Aiming at alleviating the energy consumption challenge in a multi-task FL environment, we design an automatic Multi-Task FL Deployment (MFLD) algorithm to reach the local balancing and energy consumption goals. The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. Extensive experiments validate our proposed approach and showed significant improvements in task deployment success rate and energy consumption cost.

Pattern formation in odd viscoelastic fluids

Nonreciprocal interactions fueled by local energy consumption can be found in biological and synthetic active matter at scales where viscoelastic forces are important. Such systems can be described by “odd” viscoelasticity, which assumes fewer material symmetries than traditional theories. Here we study odd viscoelasticity analytically and using lattice Boltzmann simulations. We identify a pattern-forming instability which produces an oscillating array of fluid vortices, and we elucidate which features govern the growth rate, wavelength, and saturation of the vortices. Our observation of pattern formation through odd mechanical response can inform models of biological patterning and guide engineering of odd dynamics in soft active matter systems. Published by the American Physical Society 2024

Node Localization Method in Wireless Sensor Networks Using Combined Crow Search and the Weighted Centroid Method.

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b.

Enhancing low-carbon building operations leveraging demand response driven by renewable energy consumption contributions

In the pursuit of sustainable low-carbon energy practices, demand response (DR) has emerged as a pivotal strategy, particularly in the management of building energy consumption. However, current demand management methods often lack a thorough evaluation of building carbon reduction benefits and fall short in promoting grid decarbonization. This paper proposes a low-carbon operation optimization method for buildings, with a novel DR strategy aimed at facilitating local renewable energy consumption and system-level renewables integration. Considering local and systemic renewable shares, an evaluation framework is first introduced for quantitative building energy assessment in each period. The building loads with controllable flexibilities are then grouped and aggregated based on their elasticity characteristics and probability distribution. Finally, a low-carbon optimization model is established, leveraging the DR strategy for sustainable building operations through flexible load regulation. Utilizing measured building data in Shanghai, a case study with extensive analysis validates the effectiveness of the methodology. The results suggest that following DR implementation, the similarity between the building load profile and the systemic renewable share improves by 34.48 %. Moreover, carbon emissions decrease by 5.27 % at the same level of building energy consumption. Further analysis highlights that energy storage can enhance load adjustment capabilities and the potential for reducing carbon emissions. Compared to conventional DR programs, the proposed strategy demonstrates significant advantages of abundant flexibility and low-carbon benefits. The widespread implementation of low-carbon operations in large-scale building groups has profound implications for integrating renewable energy and achieving system decarbonization goals.© 2017 Elsevier Inc. All rights reserved.

Energy rights trading policy, spatial spillovers, and energy utilization performance: Evidence from Chinese cities

China initiated its first energy rights trading (ERT) pilot in 2016 and had plans to assess the policy's replicability in 2020. However, there is limited literature available that examines its influence on energy utilization performance (EUP). To complement this knowledge gap, the study investigates the impacts and mechanisms of ERT on EUP employing a spatial difference-in-difference (SDID) model. The findings reveal that: (1) ERT reduces local energy consumption significantly, increases green total factor energy efficiency, and enhances the EUP of neighboring cities within an 1100 km radius. (2) ERT indirectly enhances the EUP of local and neighboring cities through green innovation, transformation towards cleaner energy structures, and energy allocation. Notably, ERT boosts the local EUP via industrial structure upgrading but reduces the positive spillover effects on nearby cities due to the siphoning effect. (3) ERT exhibits more pronounced impacts and spillover effects on cities that are not resource-dependent and have higher levels of marketization. Interestingly, the central and western regions benefit more from the spillover effects of ERT. The findings of this study will serve as a valuable resource for advancing the implementation of ERT and offer critical insights for decision-makers focused on sustainable urban development.

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

Grid Model of Energy Consumption Using Random Forest by Integrating Data on the Nighttime Light, Population, and Urban Impervious Surface (2000–2020) in the Guangdong–Hong Kong–Macau Greater Bay Area

Energy consumption is an important indicator for measuring economic development and is closely related to the atmospheric environment. As a demonstration zone for China’s high-quality development, the Guangdong–Hong Kong–Macao Greater Bay Area imposes higher requirements on ecological environment and sustainable development. Therefore, accurate data on energy consumption is crucial for high-quality green development. However, the statistical data on local energy consumption in China is insufficient, and the lack of data is severe, which hinders the analysis of energy consumption at the metropolitan level and the precise implementation of energy policies. Nighttime light data have been widely used in the inversion of energy consumption, but they can only reflect socio-economic activities at night with certain limitations. In this study, a random forest model was developed to estimate metropolitan-level energy consumption in the Guangdong–Hong Kong–Macao Greater Bay Area from 2000 to 2020 based on nighttime light data, population data, and urban impervious surface data. The estimation results show that our model shows good performance with an R2 greater than 0.9783 and MAPE less than 9%. A long time series dataset from 2000 to 2020 on energy consumption distribution at a resolution of 500 m in the Guangdong–Hong Kong–Macao Greater Bay Area was built using our model with a top-down weight allocation method. The spatial and temporal dynamics of energy consumption in the Greater Bay Area were assessed at both the metropolitan and grid levels. The results show a significant increase in energy consumption in the Greater Bay Area with a clear clustering, and approximately 90% of energy consumption is concentrated in 22% of the area. This study established an energy consumption estimation model that comprehensively considers population, urban distribution, and nighttime light data, which effectively solves the problem of missing statistical data and accurately reflects the spatial distribution of energy consumption of the whole Bay Area. This study provides a reference for spatial pattern analysis and refined urban management and energy allocation for regions lacking statistical data on energy consumption.

Coalitional game-based gain generation and distribution for collective self-consumption in an energy community

Climate change is requiring changes in energy use. The integration of renewable energy sources into the energy mix is a viable solution for electricity generation, but their intermittence forces us to imagine new ways to consume and distribute electricity. Thus, new economic models and new organizations have to be established. Energy communities are a recent legal solution to share renewable energy among local actors. These structures facilitate the set-up of collective self-consumption operations. Collective self-consumption is an interesting tool to increase local renewable energy consumption and limit energy losses on the distribution grid. Consequently, energy communities are pursuing a dual objective: reducing energy bills and the environmental footprint of the participants. However, the distribution of gains generated through collective self-consumption is one of the main obstacles to the implementation of energy communities. An interesting possibility comes from game theory, especially from cooperative games, as solutions concepts already exist to solve this kind of problem.In this paper, a payoff distribution is proposed through a collaborative game. The aim of the study is to explore ways to distribute gains generated by collective self consumptions on individual self consumptions. These gains are 8% on the total energy bill and 25% on the self-consumption rate, assuming a collective objective based on the self-consumption rate and individual objectives based on the price of energy. In order to distribute the financial gains, two major concepts in cooperative game theory are studied in this paper: the Shapley Value and the Nucleolus. The advantages and drawbacks of these concepts in an energy community context are identified, as these concepts are based on two different philosophies: The properties of individual fairness of the Shapley Value and collective fairness of the Nucleolus for energy management are therefore discussed. Furthermore, a methodology is proposed to enable a fairer distribution of payoffs according to specific energy management parameters, such as efficiency and flexibility.

Capacity Optimization Configuration for a Park-Level Hybrid Energy Storage System Based on an Improved Cuckoo Algorithm

To promote the development of green industries in the industrial park, a microgrid system consisting of wind power, photovoltaic, and hybrid energy storage (WT-PV-HES) was constructed. It effectively promotes the local consumption of wind and solar energy while reducing the burden on the grid infrastructure. In this study, the analytic hierarchy process (AHP) was used to decompose the multi-objective function into a single-objective function. The economic and environmental benefits of the system were taken as the objective function. Furthermore, the cuckoo search algorithm (CS) was used to solve the specific capacity of each distributed power source. Different scenarios were applied to study the specific capacity of microgrid systems. The results show that the equivalent annual cost of the WT-PV-HES microgrid system is reduced by 7.3 percent and 62.23 percent, respectively. The carbon disposal cost is reduced by 1.71 and 2.38 times, respectively. The carbon treatment cost is more sensitive to load changes. The solution iteration of the cuckoo algorithm is 18 times. Meanwhile, the system requires four updates of capacity allocation results for 20 years of operation. This result validates the effectiveness of the proposed model and methodology. It also provides a reference for the research and construction of capacity allocation of microgrid systems at the park level.

Optimal operation of integrated energy systems considering energy trading and integrated demand response

Currently, a large number of integrated energy systems operate independently, which is not conducive to the efficient utilization of clean energy. To fully utilize the energy resources of multiple integrated energy systems, realize the local energy consumption and efficient utilization, and further improve the flexibility on the demand side, this paper constructs an optimal scheduling framework for integrated energy systems considering multi-energy sharing and integrated demand response. A cooperative operation model of multiple integrated energy systems is established to minimize overall cost based on Nash bargaining theory. The energy-sharing problem is solved distributively via the alternating direction multiplier method and the stakeholders' privacy and security is protected well. The validity of the proposed model is verified by arithmetic analysis. Compared with the situation of independent operation and no comprehensive demand response, the operation scheduling strategy proposed in this article has better results. Implementing multi energy sharing and comprehensive demand response can save 21.45% of costs. The overall cost of the whole system has been reduced from the initial 18033.26RMB to 14164.39RMB. In addition, In the context of fluctuating renewable energy scenarios, the energy sharing strategy proposed in this article can still effectively reduce energy costs. Therefore, the energy scheduling strategy proposed in this article can promote the utilization of clean energy and has high practical value.

Optimal allocation method for MIES-based shared energy storage using cooperative game theory and CSP

To further promote the efficient use of energy storage and the local consumption of renewable energy in a multi-integrated energy system (MIES), a MIES model is developed based on the operational characteristics and profitability mechanism of a shared energy storage station (SESS), considering concentrating solar power (CSP), integrated demand response, and renewable energy output uncertainty. We propose a corresponding MIES model based on co-operative game theory and the CSP and an optimal allocation method for MIES shared energy storage. The model considers the maximum operating benefit of the SESS as the upper objective function and the minimum operating cost of the MIES as the lower objective function. First, the Karush–Kuhn–Tucker conditions of the lower-layer model are transformed into constraints of the upper-layer model, and the Big-M method is used to linearize the nonlinear problem and convert the two-layer nonlinear model into a single-layer linear model. Second, based on the Nash negotiation theory, the benefits of each IES in the MIES are allocated. Finally, the fuzzy chance constraints are used to relax the power balance constraints, and the trapezoidal fuzzy numbers are transformed into a deterministic equivalence class to assess the impact of renewable energy output uncertainty on system operation. The validity and rationality of the proposed two-layer model are verified through simulation, and the results demonstrate that the proposed shared storage capacity leasing model can effectively reduce the total operation cost, increase the profitability of the shared storage operator, and increase the utilization rate of the SESS.

Bargaining‐based energy sharing framework for multiple CCHP systems with a shared energy storage provider

AbstractIn the present day, when centralized energy storage technology is becoming increasingly mature, the cooperative energy sharing framework between the combined cooling, heating, and power (CCHP) systems and a shared energy storage provider (SESP) can effectively promote the local consumption of renewable energy and reduce the social energy cost. In this study, the optimal operation models of CCHP and SESP are first developed by considering the electric energy trading of the CCHP and the SESP. Then the CCHP–SESP cooperative operation model is established based on Nash bargaining theory, followed by equating it into two subproblems of coalition social energy cost minimization and electric energy trading payment negotiation. The alternating direction method of multipliers approach is introduced to solve the energy‐sharing problems in a distributed way. Numerical simulation results show the effectiveness of the proposed energy‐sharing framework. Simulation results show that the proposed bargaining‐based energy‐sharing framework can significantly reduce the social energy cost and improve the utilization rate of energy storage resources.