Optimal Scheduling for Flexible Regional Integrated Energy Systemwith Soft Open Point

The Regional Integrated Energy System(RIES)has brought new modes of development,utilization,conversion,storage of energy.The introduction of Soft Open Point(SOP)and the application of Power to Gas(P2G)technology will greatly deepen the coupling of the electricity-gas integrated energy system,improve the flexibility and safety of the operation of the power system,and bring a deal of benefits to the power system.On this background,an optimal dispatch model of RIES combined cold,heat,gas and electricity with SOP is proposed.Firstly,RIES architecture with SOP and P2G is designed and its mathematical model also is built.Secondly,on the basis of considering the optimal scheduling of combined cold,heat,gas and electricity,the optimal scheduling model for RIES was established.After that,the original model is transformed into a mixed-integer second-order cone programming model by using linearization and second-order cone relaxation techniques,and the CPLEX solver is invoked to solve the optimization problem.Finally,the modified IEEE 33-bus systemis used to analyze the benefits of SOP,P2G technology and lithium bromide absorption chillers in reducing systemnetwork loss and cost,as well as improving the system’s ability to absorb wind and solar and operating safety.

Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of“carbon neutrality”and“emission peak”,effectively controlling system carbon emissions has become significantly important to governments around the world.To this end,a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response(IDR)is proposed in this paper for the integrated energy system(IES),where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR.In contrast,the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables.In addition,to fully exploit the system’s emission reduction potential,a carbon trading cost model with segmented CO_(2) emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism,and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side.The CPLEX optimizer resolves the two-stage model,and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.

Scaling up private investment in low-carbon energy systems through regional cooperation: Effective trade policy measures

How to ensure energy supply and reduce environment pollution have turned into governments’ top priorities and key factors to maintain sustainable development. In this context, two major trade and investment agreements that could lead to profound influence on low-carbon energy systems development around the Asia-Pacific region are the Regional comprehensive economic partnership (RCEP) consisted of the Association of Southeast Asian Nations (ASEAN) plus Australia, China, India, Japan, New Zealand, and Republic of Korea and the Belt and road initiative (BRI) initiated by China. In order to have a smooth transition to low-carbon energy systems in Asia, besides RCEP and BRI, it is imperative to boost private sector investment. Success of encouraging private sector investment depends on appropriate government policies towards promoting innovations and reducing financial risks to private investors. The research questions that are examined in this study are: What type of policy measures affects trade in low-carbon transition, particularly renewable energy (RE) transition? How can investment signals and incentives be reframed to scale up private finance in RE? The objective is to investigate and to provide several feasible trade policy and investment policy tools for both national and regional markets that governments could adopt to accelerate the speed of private financing of the low-carbon energy industry, particularly the RE industry.

Energy Integration in South America Region and the Energy Sustainability of the Nations

The objective of this manuscript is to analyze relation involving the energy sector and socioeco-nomic growth and, then, contextualize the process of energy integration within the development policies in South America. The methodology considers data related to the world’s economy and energy consumption and energy integration policy in countries and regions;and, South America’s energy potential and the energy integration process. Results show that despite the political and institutional difficulties involving the process, energy integration can bring a lot of benefits for countries development. The process of energy integration in South America is divided in three moments, but in both periods the transnational energy projects were restricted, mostly, by a bi-lateral plan and the creation of physical links in a region. In the 21th century’s context, it should be noted Brazil’s participation which has been consolidated as a lead country in this process, and, also the IIRSA (Initiative for the Integration of Regional Infrastructure in South America, nowadays renamed as COSIPLAN) like the main initiative in energy integration in the continent, in a context where the projects are no longer limited to traditional economic blocs. Finally, we note a lack of consensus in defining a comprehensive model of integration and solving asymmetries both within countries and between them.

Optimal Scheduling of Regional Integrated Energy Systems Considering Hybrid Demand Response

Flexible load can optimize the load curve,which is an important means to promote renewable energy consumption.The peculiarities of electricity,heat,cooling and gas loads are analyzed in this paper,considering the fuzzy degree of human perception for water temperature,and the characteristic model of hot water load is established.Considering the fuzzy degree of human perception of ambient temperature,the characteristic model of cooling load is established by using PMV and PPD index.Meanwhile,considering four combinations of cut load,translatable load,transferable load and alternative load,and considering the coupling relationship of composite parts,different response models of load are established respectively.With the minimum cost of the system,including operation and compensation costs as the objective function,the optimization scheduling model of the regional integrated energy system is established,and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment.The results show that the load curve can be optimized,the flexible regulation ability of the regional integrated energy system can be enhanced,the energy loss of the system can be reduced,and the wind power consumption ability of the system can be increased by considering the integrated demand response.

Distributed Robust Optimal Dispatch of Regional Integrated Energy Systems Based on ADMM Algorithm with Adaptive Step Size

This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system(RIES).Our model regards the distribution network and each energy hub(EH)as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic(PV)power output uncertainties,with only deterministic information exchanged across boundaries.This paper also adopts the alternating direction method of multipliers(ADMM)algorithm to facilitate secure information interaction among multiple RIES operators,maximizing the benefit for each subject.Furthermore,the traditional ADMM algorithm with fixed step size is modified to be adaptive,addressing issues of redundant interactions caused by suboptimal initial step size settings.A case study validates the effectiveness of the proposed model,demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.

A study of novel real-time power balance strategy with virtual asynchronous machine control for regional integrated electric-thermal energy systems

The development of regional integrated electric-thermal energy systems(RIETES) is considered a promising direction for modern energy supply systems. These systems provide a significant potential to enhance the comprehensive utilization and efficient management of energy resources. Therein, the real-time power balance between supply and demand has emerged as one pressing concern for system stability operation. However, current methods focus more on minute-level and hour-level power optimal scheduling methods applied in RIETES. To achieve real-time power balance, this paper proposes one virtual asynchronous machine(VAM) control using heat with large inertia and electricity with fast response speed. First, the coupling timescale model is developed that considers the dynamic response time scales of both electric and thermal energy systems. Second, a real-time power balance strategy based on VAM control can be adopted to the load power variation and enhance the dynamic frequency response. Then, an adaptive inertia control method based on temperature variation is proposed, and the unified expression is further established. In addition, the small-signal stability of the proposed control strategy is validated. Finally, the effectiveness of this control strategy is confirmed through MATLAB/Simulink and HIL(Hardware-in-the-Loop) experiments.

New framework of low-carbon city development of China:Underground space based integrated energy systems

Cities play a vital role in social development,which contribute to more than 70%of global carbon emission.Low-carbon city construction and decarbonization of the energy sector are the critical strategies to cope with the increasingly serious climate change problems,and low-carbon technologies have attracted extensive attention.However,the potential of such technologies to reduce carbon emissions is constrained by various factors,such as space,operational environment,and safety concerns.As an essential territorial natural resource,underground space can provide large-scale and stable space support for existing low-carbon technologies.Integrating underground space and low-carbon technologies could be a promising approach towards carbon neutrality,and hence,warrants further exploration.First,a comprehensive review of the existing low-carbon technologies including the technical bottlenecks is presented.Second,the features of underground space and its low carbon potential are summarized.Moreover,a framework for the underground space based integrated energy system is proposed,including system configuration,operational mechanisms,and the resulting benefits.Finally,the research prospect and key challenges required to be settled are highlighted.

Low-Carbon Economic Operation Optimization of Mine Integrated Energy System Considering Energy Supply Uncertainty

Mine integrated energy system(MIES)can promote the uilliation of derived energy and achieve multi-energy complementation and ecological protection.Now it gradually becomes an important focus for scientific carbon reduction and carbon neutrality.To reduce the impact of uncertain prediction differences on the system during the process of using mine derived energy,a low-carbon economic operation strategy of MIES considering energy supply uncertainty is developed in this paper.Firstly,based on the basic structure of energy flow in MIES,the energy-carbon flow framework of MIES is established for the low-carbon operation requirements.Secondly,considering carbon emission constraints,the low-carbon economic operation optimization model(LEOOM)is bullt for MIES to minimize operation cost and carbon emission.Finally,multiple uncertainties of the system are modeled and analyzed by using the robust model under the risk aversion strategy of information gap decision theory(IGDT),and a model conversion method is designed to optimize the low-carbon economic operation model.The simulation results under three scenarios demonstrate that compared to the existed economic dispatching models,the proposed model achieves a 30%reduction in carbon emission while the operational cost of MIES only is increased by 2.1%.The model ffiently mitigates the carbon emission of the system,and the proposed uncertain treatment strategy can significantly improve the robustness of obtained operation plans.

Operation Cost Optimization Method of Regional Integrated Energy System in Electricity Market Environment Considering Uncertainty

In the electricity market environment,the regional integrated energy system(RIES)can reduce the total operation cost by participating in electricity market transactions.However,the RIES will face the risk of load and electricity price uncertainties,which may make its operation cost higher than expected.This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty.Firstly,based on the operation cost structure of the RIES in the electricity market environment,the energy flow relationship of the RIES is analyzed,and the operation cost model of the RIES is built.Then,the electricity purchase costs of the RIES in the medium-and long-term electricity markets,the spot electricity market,and the retail electricity market are analyzed.Finally,considering the risk of load and electricity price uncertainties,the operation cost optimization model of the RIES is established based on conditional value-at-risk.Then it is solved to obtain the operation cost optimization strategy of the RIES.Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy,constrain the risk of load and electricity price uncertainties,and help balance the risks and benefits.

An explainable framework for load forecasting of a regional integrated energy system based on coupled features and multi-task learning

To extract strong correlations between different energy loads and improve the interpretability and accuracy for load forecasting of a regional integrated energy system(RIES),an explainable framework for load forecasting of an RIES is proposed.This includes the load forecasting model of RIES and its interpretation.A coupled feature extracting strat-egy is adopted to construct coupled features between loads as the input variables of the model.It is designed based on multi-task learning(MTL)with a long short-term memory(LSTM)model as the sharing layer.Based on SHapley Additive exPlanations(SHAP),this explainable framework combines global and local interpretations to improve the interpretability of load forecasting of the RIES.In addition,an input variable selection strategy based on the global SHAP value is proposed to select input feature variables of the model.A case study is given to verify the effectiveness of the proposed model,constructed coupled features,and input variable selection strategy.The results show that the explainable framework intuitively improves the interpretability of the prediction model.

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system(RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction(M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search(CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.

An Analytical Method for Delineating Feasible Region for PV Integration Capacities in Net-zero Distribution Systems Considering Battery Energy Storage System Flexibility

To provide guidance for photovoltaic(PV)system integration in net-zero distribution systems(DSs),this paper proposes an analytical method for delineating the feasible region for PV integration capacities(PVICs),where the impact of battery energy storage system(BESS)flexibility is considered.First,we introduce distributionally robust chance constraints on network security and energy/carbon net-zero requirements,which form the upper and lower bounds of the feasible region.Then,the formulation and solution of the feasible region is proposed.The resulting analytical expression is a set of linear inequalities,illustrating that the feasible region is a polyhedron in a high-dimensional space.A procedure is designed to verify and adjust the feasible region,ensuring that it satisfies network loss constraints under alternating current(AC)power flow.Case studies on the 4-bus system,the IEEE 33-bus system,and the IEEE 123-bus system verify the effectiveness of the proposed method.It is demonstrated that the proposed method fully captures the spatio-temporal coupling relationship among PVs,loads,and BESSs,while also quantifying the impact of this relationship on the boundaries of the feasible region.

Trends and driving forces of low-carbon energy technology innovation in China’s industrial sectors from 1998 to 2017: from a regional perspective

Low-carbon energy technology(LC)innovation contributes to both environmental protection and economic development.Using the panel data of 30 provinces/autonomous regions/municipalities in China from 1998 to 2017,this paper constructs a two-layer logarithmic mean Divisia index(LMDI)model to uncover the factors influencing the variation of the innovation of LC in China’s industrial sectors,including the alternative energy production technology(AEPT)and the energy conversation technology(ECT).The results show that China’s industrial LC patent applications rapidly increased after 2005 and AEPT patent applications outweighed ECT patent applications all the time with a gradually narrowing gap.Low-carbon degree played the dominant role in promoting the increase in China’s industrial LC patent applications,followed by the economic scale,R&D(research and development)efficiency,and R&D share.Economic structure contributed to the increases in LC patent applications in the central and the western regions,while led to the decreases in the eastern region,the north-eastern region,and Chinese mainland.Low-carbon degree and economic scale were two main contributors to the growths of both industrial AEPT patent applications and ECT patent applications in Chinese mainland and the four regions.Several policy recommendations are made to further promote industrial innovation in China.

基于深度强化学习的电-气区域综合能源系统安全校正控制决策方法

电–气区域综合能源系统电、气相互耦合与影响,使得其安全校正控制难度大且对快速性要求高,为此,提出一种基于深度确定性策略梯度(deepdeterministicpolicy gradient,DDPG)算法的安全校正控制决策方法。首先,进行系统多能流与变量分析,建立安全校正控制的目标与约束条件。然后,构建基于DDPG的安全校正控制模型,设计目标奖励和各种约束条件奖励,提出结合基于综合灵敏度的安全校正知识经验设计目标奖励函数,使调整具有方向性,且目标奖励考虑能量枢纽(energy hub,EH)的经济效益及其可再生能源消纳;通过智能体离线训练,使其能够在线做出实时最优的安全校正控制策略,预先产生专家经验数据集存放于经验回放池,提高训练速度和收敛性。最后,通过含EH电–气区域综合能源系统仿真算例验证了所提方法的有效性。

含垃圾能源化处理及电动汽车负载的区域综合能源系统协调优化

传统能源系统互相独立、欠缺联动,容易导致能量的浪费,并且能源结构也难以优化。区域综合能源系统具备多能互补、高效利用的优点,是提高能源利用效率和环保性的重要改进技术。为了利用垃圾蕴含的能量和电动汽车负载端的能源消纳,文中建立一个考虑垃圾焚烧和电动汽车负载的区域综合能源系统模型,利用模型进行系统的优化调度研究。论文分析了系统内的多能源能量流通关系,而后对系统内的各种能源转换设备及电动汽车进行分析,引入垃圾能源化处理,将热能转化为电能,并利用电动汽车进行电能消纳的模式,得出区域综合能源系统的能源效益最大化的模型。通过python进行算例分析,结果表明,含垃圾处理和电动汽车参与的区域综合能源系统既能提高能源的利用效率,又能带来更优的经济效益。

考虑[火用]效率的区域综合能源系统配置与调度双层优化

文章针对含高比例的可再生能源的区域综合能源系统,综合考虑能量的数量和品质,提出了一种计及[火用]效率的双层优化方法。首先,建立了区域综合能源系统电、气、热、冷多能耦合的模型,并对系统内涉及的能量和设备进行[火用]分析,分别计算其能量品质系数和[火用]效率;然后,建立了兼顾系统容量规划和调度运行的双层协同优化模型,其中上层以[火用]效率和经济性为优化目标进行容量配置,下层以运行成本最低为目标优化机组出力,从而得到最佳方案;最后,选取某区域综合能源系统进行算例分析。与其他方案相比,文章所提双层优化方法可以为区域综合能源系统提供更好的配置和调度方案,[火用]效率可以更好地反映能量真实利用水平,实现高质量用能。

考虑用户需求响应博弈推演的区域综合能源运营策略

随着工商业用户能源种类多样化及其耦合程度紧密化,通过激励消费者剩余价值引导综合能源用户参与需求响应,对于降低综合能源系统运行成本以及提高用户能效具有积极意义。该文设计了基于消费者剩余的体验估值函数,并将其考虑在用户侧目标函数中,通过演化博弈推导工商业用户参与需求响应的动态过程,从而克服传统分析方法对用户侧参与需求响应的定值设置。该文建立了基于用户侧需求响应演化博弈的综合能源系统优化运营模型,上层建立了区域综合能源系统优化模型,计算面向用户的用能价格;下层建立了考虑剩余价值的区域综合能源系统用户响应模型,并进行演化博弈推导。算例基于电-热-气综合能源系统,采用演化博弈分析了需求侧市场用户响应行为及其影响因素。结果表明,工商业两类用户参与需求响应使得综合能源系统的运行总成本降低5.5%,同时两类用户效用分别提高了51.91%和44.29%。所提模型和方法不仅可以模拟用户侧需求响应动态博弈过程,而且能够为综合能源系统中运营商和需求侧用户的策略选择提供有益参考。

考虑需求响应及不确定性的综合能源优化调度

如何在区域综合能源系统中建立有效的需求侧模型,对于需求响应政策的实施至关重要。为提高用户参与需求响应积极性,引入实时定价机制,同时考虑系统的经济低碳性和电气热用户的用能满意度,构建兼顾系统和用户利益的综合需求响应主从博弈模型。针对用户参与需求响应的不确定性,引入贝叶斯方法更新负荷曲线。算例分析表明,所提博弈模型及贝叶斯方法能够有效平衡系统和用户之间的利益,提高了综合需求响应的可靠性。

基于主从博弈的含碳捕集与热电联产综合能源系统优化运行

为了合理地分配综合能源系统中各主体之间的利益,促进能源低碳化,文中基于主从博弈框架建立了含碳捕集系统的综合能源系统优化调度模型,降低了热电联产机组的碳排放量和系统的运营成本。文章构建碳捕集、电转气和热电联产的联合运行模型,在考虑综合需求响应的基础上,建立综合能源系统各主体的收益模型;构建一主多从的Stackelberg博弈模型,由于构建的优化模型具有高维、非线性非凸的特点,数值方法难以求解,传统启发式算法容易陷入局部最优且收敛速度较慢,因此提出一种新型天牛郊狼算法结合二次规划的分布式算法。以北方某工业园区为算例进行验证,分析结果表明,文中提出的模型及求解算法与传统模型相比,有效地降低了综合能源系统的运行成本和碳排放量,提高了可再生能源的消纳能力。