Integrated Electricity and Natural Gas System (IEGS) considers the interactions between electricity and natural gas systems with broad prospects in carbon emission mitigation to achieve the global low-carbon transition, which is an approachable pathway to tap the potential of different energy systems. Concurrently, advancements in technologies such as Carbon Capture, Utilization and Storage (CCUS), Gas-fired Power Generation (GPG), and Power to Gas (PtG) enable the integration of these two large systems, allowing for bi-directional energy flows. This paper proposes an original IEGS retrofit planning model, in which the traditional power plant/gas source (PP/GS) is retrofitted into the carbon capture power plant/carbon capture gas source (CCPP/CCGS) with CCUS and PtG/GPG, as well as the gas pipelines and electricity transmission lines, are considered. Additionally, the IEGS retrofit model employs a bi-level planning strategy to distinguish conflicts of interest between investors and investees. Furthermore, the reformulation and decomposition (R&D) algorithm is developed to tackle the complexities of the bi-level mixed-integer programming problem. Numerical results demonstrate the effectiveness and superiority of the proposed model, showcasing its potential for practical application. Finally, the study analyzes the efficient boundaries associated with carbon price/tax and carbon capture/storage cost, providing valuable insights for policymakers and stakeholders.

Synergy among renewable energy sources (RESs), energy network (EN) expansion and electrified transport provides a potential pathway toward low-carbon energy systems. However, the ambition of deep decarbonization may not be achieved by connecting all stakeholders through single market-wise effort. To this end, this article presents a coordinative planning framework of public transport electrification (PTE), RESs and ENs for decarbonization of urban multi-energy systems (UMESs). The framework is formulated as a three-level programming and driven by both government incentives and market signals. In the upper-level, the investment decision models of RESs, ENs and PTE are developed. Particularly, public transport hubs (PTHs) are viewed as flexible multi-energy demands whose potential of implementing demand response is incorporated in PTE planning model. In the middle-level, a carbon emission-security assessment subproblem is proposed, where market equilibriums are captured by Karush-Kuhn-Tucker optimality conditions; then the nested Benders decomposition and Lagrangian relaxation approach are utilized to synthetize capacity incentives paid by governments. In the lower-level, a hybrid energy pricing mechanism is designed to obtain integrated energy-carbon prices. The above hierarchical model is solved by an iterative algorithm where all investors update their planning decisions in a decentralized manner based on customized government incentives and market prices. Numerical studies prove that our proposed approach can effectively coordinate all stakeholders and promote the low-carbon transition of UMESs.

Aiming at the decision of energy-saving renovation scheme of public buildings, based on the comprehensive income of energy-saving and carbon trading, this paper establishes the energy-saving, carbon reduction and economic benefit models of six technologies, such as: enclosure structure ( trading, this paper established the energy-saving, carbon reduction and economic benefit models of six technologies, such as: enclosure structure ( wall and form) renovation, lighting renovation, air conditioning optimization, air-conditioning optimization, energy-saving and carbon trading. wall and form) renovation, lighting renovation, air conditioning optimization, roof PV and boiler power replacement. The decision-making method of transformation scheme under the fluctuation of energy-saving and carbon trading is as follows The decision-making method of transformation scheme under the fluctuation of electricity price and carbon trading price is constructed with the help of multiple regression algorithm. Breaking through the traditional experience empowerment evaluation model, the optimal initial investment amount and the weighting and priority of various technology selection decisions are deduced. Breaking through the traditional experience empowerment evaluation model, the optimal initial investment amount and the weighting and priority of various technology selection decisions are deduced. Finally, the effectiveness of the method is verified by combining engineering cases, aiming to provide reference for owners to Finally, the effectiveness of the method is verified by combining engineering cases, aiming to provide reference for owners to flexibly respond to the energy and carbon trading market and precise energy saving.

This study presents an optimal electricity-heat system (EHS) planning framework to promote the accommodation of wind power while considering technical, economic and environmental criteria. To this end, integrated demand response (IDR) is introduced as a flexibility resource to complement the inherent fluctuation of renewable energy sources and modeled by using price elasticity theory. Both the timing transferring and energy substitution potentials are considered in the proposed IDR program. Incorporating the effect of IDR into the EHS planning model, a two-stage stochastic programming model can be devised, in which the optimal EHS configuration design and associated operation control techniques are found simultaneously to minimize the system's total economic and carbon-emission costs over the planning horizon. The multi-scale uncertainties arising from both long-term demand growth and operation-level variability of renewables/load demands are captured collectively by using a scenario-based method. The suggested planning approach is illustrated using a real EHS test case and the results show that it is effective in practical applications.

The grid-connection of high penetration renewable energy makes a higher request on the flexibility of power grid.During the planning stage of renewable energy system taking collaborative optimization of multiple flexible resources into consideration can effectively improve the system flexibility.For this reason, based on the analysis on flexible regulating ability, considering flexibility a bi-level planning model of distributed generation in distribution network was proposed. Taking economic goals and flexible goals as optimization objectives, a multi scenario coordinated optimization planning model was constructed. Considering the defect of low solution efficiency due to too large scenery scene set, on the basis of affinity propagation（abbr. AP） clustering algorithm an APKmedoids-based bi-level scene reduction technology was put forward, and the reduced scene was verified. Finally, by use of the mixed solution strategy of integer adaptive particle swarm optimization（abbr. APSO） and chaos particle swarm optimization（abbr. CPSO） the simulation of the proposed bi-level programming model was implemented. Simulation results show that the proposed programming method is effective in improving economy and flexible regulation ability.

New energy power plants generate vast amounts of operational data and experience highly variable operating conditions， posing a significant challenge when it comes to diagnosing faults in generator sets. For this reason， a fault diagnosis model based on an improved SVM （support vector machine） algorithm is introduced. Firstly， the concept and principle of SVM for new energy power plants are analyzed. A multivariate SVM classifier is used to optimize the SVM. Secondly， the fault signal extraction method and fault characterization method of photovoltaic power stations and wind farms are studied. Moreover， a fault diagnosis model is proposed. Finally， sample data are obtained from new energy power plants， and an improved SVM fault diagnosis model based on decision level fusion is built. Additionally， the model is trained using fault feature vectors. The results show that the fault diagnosis accuracy for photovoltaic power plants reaches 97.5%， while the fault diagnosis accuracy for wind farms reaches 98.09%， which verify the accuracy of the method.

The micro-energy grid can meet various load demands and realize the complementary advantages of different energy sources, which provides a new way to solve the problems of energy utilization efficiency and environ-mental pollution. However, how coordinating multiple energy sources and improving the flexibility of the micro -energy grid is an urgent problem to be solved. This paper proposes an optimal scheduling model based on chance-constrained programming (CCP), which considers electric vehicle (EV) charging characteristics, inte-grated electricity-heat demand response, and ladder-type carbon trading in the background of various renewable uncertainty. Firstly, this paper uses power to gas (P2G) technology and combined heat and power (CHP) tech-nology to improve the flexibility of the system and realize the coupling between different energy sources. Sec-ondly, integrated demand response (IDR) is used to explore potential interaction capabilities between electric -heat flexible load and micro-energy grid. Then, the ladder-type carbon trading mechanism is introduced in the optimization scheduling model to reduce the carbon emissions of the system. Finally, sequence operation theory (SOT) transforms the original CCP model into a conveniently solvable mixed-integer linear programming (MILP) model. The simulation results show that all subsystems are closely coupled due to the participation of P2G and CHP, which reduces the operation cost of the system by 3.9 %. The results also indicate that the IDR mechanism improves energy efficiency and reduces operating costs by 7.8 %. Finally, the results substantiate that the ladder-type carbon trading mechanism reduces the carbon emissions of the system by 18.1 % and improves the environmental benefits of the system.

Growing penetration of renewable energy sources (RES) and emerging electrified loads (EEL) are bringing about increased difficulties for the power balancing and efficient operation of energy system, due to the impact of remarkable volatilities introduced. Public transport hub (PTH), as a new-style infrastructure of traffic service carriers, is regarded to offer a cogent solution to this problem, in terms of their potential operational flexibilities permitted by energy regulation, vehicular dispatch, and vehicle-to-grid (V2G) programs. As such, this paper carries out a comprehensive study to investigate the implication of harnessing PTH-enabled flexibility in a context of urban multi-energy system (UMES). The proposed methodology is established on a holistic reliability/economic analysis framework which is designed to indicate how UMES’s performances would vary with different utilization of PTH resources. In order to portray the real-time controllability of PTH during operation, a PTH model that takes into account the impacts of both energy- and service-related aspects has been developed, with particular focus on the interdependencies between the energy and transportation sector. The operational simulation of UMES in presence of PTHs is implemented by using a multi-modal-based optimization model, which captures the effects of PTH flexibility under both normal and contingency scenarios integratedly. By embedding the above formulation into a sequential Monte Carlo simulation-based assessment framework, the contribution of PTH to the reliability and economy of UMES can be determined. Numerical studies are conducted based on an illustrative electricity-gas-heat test case and the real PTH datasets in Beijing. The simulation results confirm the significance of PTH-enabled flexibility in improving the performances of UMES. Also, it is demonstrated that the reserving strategy adopted, the composition of vehicle model, and the travel demand profile of passengers are the noteworthy factors that influence the profitability of PTH exploitation.

This paper proposes a comprehensive framework to investigate the potential role of grid-connected battery swapping station (BSS) with vehicle-to-grid (V2G) in improving the economy and reliability of the distribution system. For this aim, we first develop an empirical operating model considering operating characteristics of the distribution system with the BSS explicitly. Then, on this basis, a rolling optimization scheduling model of the distribution network is established. In particular, a refined BSS model including a new calculation method of available generation capacity (AGC) is included. Finally, a quantitative method to quantify the effect of BSS on reliability and economy of distribution networks is proposed. Compared with existing works, the main contribution of this paper is threefold. (i) A battery-level BSS operational model including AGC calculation is proposed to fully explore the V2G potential of the BSS. (ii) A rolling optimization scheduling model is established, which fully exploits the potential interaction between economy and reliability of system operation. (iii) An assessment framework is proposed to analyze the special value of BSS resources from aspects of both economy improvement and reliability enhancement, which can provide more practical instruction for BSS planning. Numerical studies are conducted based on IEEE 33-bus distribution network to verify the accuracy and efficiency of our approach. The simulation results show that if V2G of the BSS is orderly exploited and managed, the net profit of the distribution system will increase by 11.97%, and system expected energy not supplied (SEENS) will decrease by 14.34%. The reliability and economy are two contradictory factors, the dispatch model we proposed can achieve better economy with the same reliability level. The location of the BSS also has important effect of the economy and reliability of distribution system. (c) 2017 Elsevier Inc. All rights reserved.

This paper proposes a methodological framework to quantify the economic-flexibility gains from the flexible resource exploitation of battery swapping stations (BSS). We first develop an overall battery-based BSS operational model. Then a joint optimal dispatch model of distribution system with BSSs oriented to both normal and contingency state is proposed. The model is aggregated into a rolling optimization to deal with the uncertainty of the system, such as time and duration of system failures. Embedding rolling optimization into sequential Monte Carlo simulation (SMCS), we propose a systematic approach of economic-reliability evaluation of distribution system with BSSs. The analysis of numerical shows that the flexibility exploitation of BSSs can significantly improve the reliability and economy of the distribution network. However, reliability and economic performance are a pair of factors that restrict each other, which are also affected by the preference of the decision maker.

In order to further improve the energy consumption potential and system energy utilization efficiency on the demand side of regional integrated energy system (RIES), taking into account economy and energy efficiency, a RIES optimal operation model and strategy considering integrated demand response (IDR) and exergy efficiency are proposed. Firstly, the IDR is modeled, the exergy efficiency of RIES is analyzed, and an exergy efficiency model considering IDR is established. Secondly the RIES multi-objective optimization model is established with the goal of the lowest economic cost and the highest exergy efficiency. In order to obtain a series of Pareto frontier solutions with uniform distribution, the normal boundary intersection is used to solve the multi-objective. Finally, the system economic cost and exergy efficiency under four different scenarios are analyzed. A practical example is used to verify the proposed model. The results show that considering the comprehensive demand response in RIES can effectively reduce the system cost. Through multi-objective analysis, it is verified that the reasonable addition of comprehensive demand response can further improve the system exergy efficiency and stimulate the exergy consumption potential on the demand side.

To address the problem of low carbon economic dispatch of integrated energy systems, carbon emission flow theory is introduced into the optimal dispatch of integrated energy systems. Firstly, the carbon emission flow model is used to calculate the carbon emissions of each load in the framework of the electricity-gas integrated energy system. Secondly, the Shapley value method is introduced into the integrated energy system carbon trading model to analyze its incentive effect on emission reduction. Finally, a bi-level dispatching model of the integrated energy system is established, with the upper level being the grid and gas network dispatching model with the minimum operating cost as the objective function, and the lower level being the energy hub dispatching model with the minimum operating cost as the objective function. Low carbon economic dispatching is carried out with the objective function of minimizing operating costs. The model is verified through arithmetic examples to have the effect of improving energy utilization, reducing operating costs, and reducing carbon emissions, and can achieve low carbon economic operation.

针对动力电池衰退规律的不一致性和梯次利用寿命短等问题,提出了基于衰退速度预测的退役电池剩余价值优化方法.首先,采用灰色预测和最小二乘支持向量机的组合挖掘电池历史使用数据,预测退役电池的衰退规律;其次,以退役电池利用效益最高为目标函数,兼顾退役电池的折损成本,提出了全寿命周期下退役电池组动态运行方案;最后,使用动态数据实现滚动预测.以长三角某公交示范站为例进行仿真,结果表明所提方法可有效预测电池衰退规律,退役电池的收益提高10％.

Demand response is an important way to balance the power supply and demand， and to wake up user-side resources. In view of data trust， privacy protection， transaction efficiency and other issues of current demand response business， a solution based on smart contract on blockchain is proposed. First， according to the invited demand response mechanism， the pain points and difficulties of the current business mechanism are analyzed. Then， the smart contract on blockchain is introduced to construct the technical framework of the invited demand response framework based on smart contract， to design the operation mechanism and the related smart contract functions.， and to analyze the technical advantages of the smart contract on blockchain applied in the demand response business. Finally， the deficiencies of this mechanism are discussed， and the future development direction of demand response business based on blockchain technology is discussed concerning demand response service increase， transaction system improvement， user-side interaction enhancement， and timeliness advancement.

With the goal of carbon neutralization clear, reducing the social energy cost through technical means is of great significance for energy low-carbon transformation. Based on this background, this paper proposes a Conservation Voltage Reduction (CVR) operation method for medium-voltage power distribution system. CVR refers to operating under a lower voltage range on the premise of satisfying the supply voltage deviation, so as to achieve the purpose of energy saving and consumption reduction. The method proposed in this paper adopts the load exponential model and realizes the voltage reduction and energy saving operation through the adjustment of On-Load Tap Changer (OLTC). Coupled with the reactive compensation of the capacitor bank, network reconstruction, and distributed generations (DGs), the regulation guarantees that the voltage reduction operation meets the power quality requirements of the power supply to achieve the minimum total energy consumption of the distribution system within a period of time. Through the analysis of IEEE 33 node distribution system examples, the effectiveness and rationality of the proposed method are verified.

In this paper, a novel methodological framework for energy hub (EH) planning, considering the correlation between renewable energy source (RES) and demand response (DR) uncertainties, is proposed. Unlike other existing works, our study explicitly considers the potential correlation between the uncertainty of integrated energy system operations (i.e., wind speed, light intensity, and demand response). Firstly, an EH single-objective interval optimization model is established, which aims at minimizing investment and operation costs. The model fully considers the correlation between various uncertain parameters. Secondly, the correlation between uncertainties is dealt with by the interval models of multidimensional parallelism and affine coordinate transformation, which are transformed into a deterministic optimization problem by the interval order relationship and probability algorithm, and then solved by a genetic algorithm. Finally, an experimental case is analyzed, and the results show that the research method in this paper has good engineering practicability. At the same time, different correlations among uncertainties have different influences on integrated energy system planning. Correlation and influence are positively correlated.

In the future smart cities, parking lots (PLs) can accommodate hundreds of electric vehicles (EVs) at the same time. This trend creates an opportunity for PLs to serve as a potential flexibility resource, considering growing penetration of EVs and integration of distributed energy resources DER (such as photovoltaic and energy storages). Given this background, this paper proposes a comprehensive evaluation framework to investigate the potential role of DER-integrated PLs (DPL) with the capability of vehicle-to-grid (V2G) in improving the reliability of the distribution network. For this aim, first, an overview for the distribution system with DPLs is provided. Then, a generic model for the available generation capacity (AGC) of DPLs with consideration of EV scheduling strategy is developed. On the above basis, an iterative-based algorithm leveraging sequential Monte Carlo simulation is presented to quantify the contribution of DPLs to the reliability of the system. In order to verify the effectiveness of the proposed method, a series of numerical studies are carried out. The simulation results show that the integration of DPLs with the V2G capability could help to improve the reliability performance of distribution grid to a great extent and reduce the adverse impact incurred by EV accommodation, if utilized properly.

退役动力电池单元不一致性严重影响了电池组的剩余寿命和容量利用率,且对均衡管理系统提出了更高的要求.结合电池的开路电压-荷电状态(ocv-soc)特性曲线,提出了一种基于电压和soc的多变量协调均衡控制策略,根据电池组soc所处的状态,选取电压和soc的组合权重作为均衡变量.仿真结果表明,多变量协调均衡策略与单一变量均衡策略相比,以相对较快的均衡时间实现了电池电压和soc更好的均衡效果,满足了退役电池组均衡的实际需求.

针对综合能源利用效率,区别于传统能源效率计算,提出基于热力学第二定律的综合能效计算方法,首先建立考虑能源能质系数的工商业园区模型,能源的利用效率不仅要从数量上,更要从质量上考虑,以综合能源效率最大和碳排放最小为目标函数,结合加权法在MATLAB/Cplex软件上进行求解.结果表明,系统中加入能质系数后,可更加准确地衡量各设备的出力情况,在多目标分析中,考虑能质系数能够有效减少碳排放并且可提高能源的梯级利用.