A risk aversion optimal model for microenergy grid low carbon‐oriented operation considering power‐to‐gas and gas storage tank

Power‐to‐gas storage (P2GS) provides a new approach for clean energy accommodation, which could be used to support the development of the microenergy grid (MEG). This paper designs a novel structure of MEG including power sources, heating sources, and P2GS. The risk condition values (CVaR) is applied to analyze the uncertainties from wind power plant, photovoltaic power generation, and solar collectors, and a multiobjective optimal model is proposed for balancing the operation economic benefit and the risk level. Then different membership functions are selected for converting the multiobjective model into a mixed integer programming model. Finally, an industrial park in northern China was selected to verify the proposed model. The results show that (a) complementary effect exists in different clean energy sources and P2GS can convert excess energy into CH₄ for power generation, which are better for achieving the power‐to‐gas‐to‐power cycle optimization effect. (b) The proposed optimal model could balance revenue and risk, rationally control its operation risk while pursuing the maximum operation benefits, which is better to achieve MEG's optimal equilibrium operation. (c) Maximum emission trade allowance could enhance MEG's enthusiasm for clean energy accommodation, and P2GS could be a feasible way for near‐zero carbon emissions. Therefore, the proposed risk aversion optimal model could increase the economic benefits and control the risks and provide decision support for decision makers.     

Risk‐based optimal energy management of virtual power plant with uncertainties considering responsive loads

This paper proposes a stochastic scheduling model to determine optimal operation of generation and storage units of a virtual power plant (VPP) for participating in a joint energy and regulation service (RS) market under uncertainty. Beside electricity, the VPP provides required RSs according to the probability of delivery request in the electricity market. A new model for providing RS is introduced in which the dispatchable generation units are financially compensated with their readiness declarations and will be charged/paid for their real‐time down/up regulations. Besides, the VPP sets up incentive price‐quantity curves to benefit from the potential of demand side management in both energy and RS market. Within the model presented here, the VPP consists of two types of generation units: wind turbine and standby diesel generator; the latter is modeled by considering CO₂‐emission penalty costs. The given uncertainties are divided into two parts. Firstly, the uncertainties from the energy market price are simulated using information gap decision theory to evaluate the risk‐based resource scheduling for both risk‐taker and risk‐averse VPP. Other uncertainties affecting decision making such as wind turbine generation, load, regulation up/down calling probabilities, and regulation market prices are modeled via scenario trees. Three typical case studies are implemented to validate the performance and effectiveness of the proposed scheduling approach.     

基于CVaR方法的风力发电项目投资风险度量

风力发电项目具备较为独立的特点,将CVaR方法引入风力发电项目的投资风险度量,不仅可以全面衡量技术风险、经济风险、政策风险等相关风险因素,同时可以最大限度地考虑投资者的损失承受底线。借助蒙特卡洛方法模拟风力发电的上网电量,很好地计量了风力发电的不确定性所带来的风险。以内蒙古地区某一风力发电项目为实例,分别计算了其VaR值和CVaR值。计算结果表明,CVaR方法能够更加谨慎有效地估计风力发电项目投资者的潜在损失。文章进一步研究了贴现率和电价等风险因素对风力发电项目整体风险的影响,认为较低的贴现率和较高的电价水平能够帮助投资者很好地规避投资风险。     

北方清洁供暖现状和趋势分析

冬季供暖是我国季节性能源需求波动的重要因素,推进冬季清洁取暖,关系北方地区广大群众温暖过冬、雾霾天能不能减少等问题,是能源生产和消费革命、农村生活方式革命的重要内容.本文梳理了我国冬季供暖工作近年来的进展和成绩,对我国供热热源结构、供热能效进行分析,提出了改进方向,并指出在推进清洁取暖中,扩大集中供热规模,推进煤改电、...     

考虑需求侧无功不确定性的负荷聚合商运营优化模型

针对需求侧多种类负荷不确定性给负荷聚合商在运营过程所带来的挑战,建立考虑需求侧不确定性的负荷聚合商运营优化模型。首先,考虑需求侧用电特征,建立具有不确定性的负荷聚合模型;进而,考虑需求侧无功不确定性,以负荷聚合商收益最大化为目标,建立基于条件风险价值的负荷聚合商运营优化模型;最后;选取蒙东某配网地区的风光、负荷及实时电价数据,通过仿真对比验证所提出的运营优化模型可有充分利用需求侧响应为电网带来的调节能力,有效提高负荷聚合商的运营收益。     

Evaluation of hydrogen storage technology in risk-constrained stochastic scheduling of multi-carrier energy systems considering power,gas and heating network constraints

The operation of energy systems considering a multi-carrier scheme takes several advantages of economical, environmental, and technical aspects by utilizing alternative options is supplying different kinds of loads such as heat, gas, and power. This study aims to evaluate the influence of power to hydrogen conversion capability and hydrogen storage technology in energy systems with gas, power, and heat carriers concerning risk analysis. Accordingly, conditional value at risk (CVaR)-based stochastic method is adopted for investigating the uncertainty associated with wind power production. Hydrogen storage system, which can convert power to hydrogen in off-peak hours and to feed generators to produce power at on-peak time intervals, is studied as an effective solution to mitigate the wind power curtailment because of high penetration of wind turbines in electricity networks. Besides, the effect constraints associated with gas and district heating network on the operation of the multi-carrier energy systems has been investigated. A gas-fired combined heat and power (CHP) plant and hydrogen storage are considered as the interconnections among power, gas and heat systems. The proposed framework is implemented on a system to verify the effectiveness of the model. The obtained results show the effectiveness of the model in terms of handling the risks associated with multi-carrier system parameters as well as dealing with the penetration of renewable resources.     

A comparison of variation management strategies for wind power integration in different electricity system contexts

Variation management strategies improve the capability of the electricity system to meet variations both in the electricity demand and in the generation that relies on variable energy sources. In this work, we introduce a new, functionality‐based, categorization of variation management strategies: shifting (eg, batteries), absorbing (eg, power‐to‐gas), and complementing (dispatchable generation, including reservoir hydropower) strategies. A dispatch model with European coverage (EU‐27 plus Norway and Switzerland) is applied to compare the benefits of shifting and absorbing strategies on wind integration in regions with different amounts of complementing strategies in place. The benefits are measured in terms of the wind value factor, wind owner revenue, and average short‐term generation cost. The results of the modeling show that the reduction in average short‐term generation cost and the increase in revenue earned by the wind owner from shifting strategies, such as the use of batteries, are more substantial at low wind shares than at high wind shares. The opposite situation is found for absorbing strategies, such as power‐to‐gas, which are found to be more efficient at reducing the average generation cost and increasing profit for the wind owner as the wind share increases. In regions that have access to complementing strategies in the form of reservoir hydropower, variation management has a weak ability to reduce the average short‐term generation cost, although it can increase significantly the revenue accrued by the wind power owner.     

Hedging strategies in energy markets: The case of electricity retailers

As market intermediaries, electricity retailers buy electricity from the wholesale market or self-generate for re(sale) on the retail market. Electricity retailers are uncertain about how much electricity their residential customers will use at any time of the day until they actually turn switches on. While demand uncertainty is a common feature of all commodity markets, retailers generally rely on storage to manage demand uncertainty. On electricity markets, retailers are exposed to joint quantity and price risk on an hourly basis given the physical singularity of electricity as a commodity. In the literature on electricity markets, few articles deal on intra-day hedging portfolios to manage joint price and quantity risk whereas electricity markets are hourly markets. The contributions of the article are twofold. First, we define through a VaR and CVaR model optimal portfolios for specific hours (3 am, 6 am,. . . ,12 pm) based on electricity market data from 2001 to 2011 for the French market. We prove that the optimal hedging strategy differs depending on the cluster hour. Secondly, we demonstrate the significantly superior efficiency of intra-day hedging portfolios over daily (therefore weekly and yearly) portfolios. Over a decade (2001–2011), our results clearly show that the losses of an optimal daily portfolio are at least nine times higher than the losses of optimal intra-day portfolios.     

Effect of apartment building energy renovation on hourly power demand

ABSTRACT

Optimal energy renovations of apartment buildings in Finland have a great impact on annual energy demand. However, reduction of energy demand does not necessarily translate into similar changes in peak power demand. Four different types of apartment buildings, representing the Finnish apartment building stock, were examined after optimal energy retrofits to see the influence of retrofitting on hourly power demand. Switching from district heating to ground-source heat pumps reduced emissions significantly under current energy mix. However, the use of ground-source heat pumps increased hourly peak electricity demand by 46–153%, compared to district heated apartment buildings. The corresponding increase in electrical energy demand was 30–108% in the peak month of January. This could increase the use of high emission peak power plants and negate some of the emission benefits. Solar thermal collectors and heat recovery systems could reduce purchased heating energy to zero in summer. Solar electricity could reduce median power demand in summer, but had only a little effect on peak power demand. The reduction in peak power demand after energy retrofits was less than the reduction in energy demand.     

Priority and delay constrained demand side management in real‐time price environment with renewable energy source

In smart grid, integration of renewable energy sources such as solar and wind is a challenging task because of their intermittent nature. Most of the existing demand side management techniques are based on day‐ahead pricing or time of use pricing that deviate from real‐time pricing because of unpredictable energy consumption trends and electricity prices. This paper presents opportunistic scheduling algorithms in a real‐time pricing environment based on optimal stopping rule. We classify different users and assign priorities based on energy demand. In order to minimize the electricity bill and appliance waiting time cost, we modify the first come first serve scheduling algorithm. Regarding comfort maximization, priority enable early deadline first scheduling algorithm is proposed, which schedules the appliances based on minimum length of operation time and priority constraints. Simulation results validate the effectiveness of the proposed algorithms in terms of electricity cost reduction and user comfort maximization. Copyright © 2016 John Wiley & Sons, Ltd.     

基于峰谷分时电价的虚拟电厂日前申报及运行优化

鉴于分布式能源的出力性质为间歇性和波动性,影响电网安全高效运行,而虚拟电厂可解决此问题。对虚拟电厂进行申报-调度两阶段建模,以包含分布式电源(光电、风电和燃气轮机)和储能系统(储能电池)两大类分布式能源的虚拟电厂为例,基于光照强度及风速等不确定因素,研究峰谷分时电价下日前市场虚拟电厂的出力申报和运行调度策略并利用风险价值模型进行风险管理。结果表明,申报出力时,当电价激励足够高时储能电池方会参与出力,成功起到削峰填谷的作用;运行调度时,平衡出力偏差方面,储能电池发挥主要作用,且虚拟电厂运行调度策略具有一定的经济效益。     

Risk-averse based optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel hybrid energy system in the electricity/hydrogen markets

The techno-economic advantages of grid-connected hybrid energy system (HES) exploit synergies to improve reliability and economic efficiency while maintaining grid stability. Therefore, this paper proposes a risk-averse optimal operational strategy of grid-connected photovoltaic/wind/battery/diesel HES to participate into two energy markets including electricity and hydrogen markets. The grid company can flexibly trade power into two markets to maximally achieve profits based on price arbitrage. The risk influences of the uncertainties, i.e., photovoltaic/wind generation, and electricity prices on the expected revenue are evaluated with CVaR model. For a better exhibition of seasonal variability effects on HES optimal operation strategy, two typical Spring/Summer days are chosen. The proposed risk-averse optimal operational strategy is formulated as a two-stage mixed-integer linear programming (MILP) model. The results in a Spring day simulation under non-risk situation indicate that the overall expected revenue can be improved 2.74 times larger if considering hydrogen market. Moreover, the optimal operational strategy of hydrogen production is considerably affected by unpredictable wind farm. Sensitivity analysis also validates that the changes of PV/WT curtailment penalty have a profound influence than battery degradation coefficient on the HES expected revenue.     

Two-stage stochastic-based scheduling of multi-energy microgrids with electric and hydrogen vehicles charging stations,considering transactions through pool market and bilateral contracts

In order to mitigate greenhouse gas emissions and improve energy efficiency, sustainable energy systems such as multi-energy microgrids (MEMGs) with the high penetration of renewable energy resources (RES) and satisfying different energy needs of consumers have received significant attention in recent years. MEMGs, by relying on renewable resources and energy storage systems along with energy conversion systems, play an essential role in sustainability of energy supply. However, renewable energies are uncertain due to the intermittent nature of solar and wind energy sources. Thus, optimal operation of the MEMGs with the consideration of the uncertainties of RES is necessary to achieve sustainability. In this paper, risk constrained scheduling of a MEMG is carried out with the presence of the PV, wind, biomass, electric vehicles (EVs) and hydrogen vehicles (HVs) charging stations, combined heat and power (CHP), boiler, hydrogen electrolyzer (HE), cryptocurrency miners (CMs), electrical, thermal and hydrogen storage systems, responsive demands. From the trading and business model side, the proposed MEMG optimized operation relies on bilateral contracts between producers and consumers and pool electricity markets. A two-stage stochastic programming method is used for considering the uncertainties of electrical, thermal and hydrogen demands, EV and HV charging stations load, CM load, PV and wind power, and the price of electricity purchased from the pool market. The proposed mixed integer linear programming (MILP) model is solved using the CPLEX solver in GAMS which guarantees to achieve a globally optimal solution. The results show that due to the certain prices of bilateral contracts, the possibility of transaction by bilateral contracts decreases the risk metric CVaR by 50.42%. The simulation results demonstrate that risk of high operation costs while considering flexibility sources, such as storages and demand response (DR) programs, is decreased by 5.45% and 4.6%, respectively. As far as operation costs are concerned, results reveal that using renewable resources decreases operation costs by 34.47%. Moreover, the operation cost is reduced by 5.94% and 4.57% in the presence of storage units and DR programs, respectively. In the same way, storages and DR programs decrease cost of purchased electricity by 13.47% and 14.46%, respectively.     

高比例可再生能源渗透下多虚拟电厂多时间尺度协调优化调度

考虑可再生能源出力、电力负荷和电价等一系列不确定性因素,提出了高比例可再生能源渗透下的多虚拟电厂日内两阶段优化调度模型.该模型通过对多虚拟电厂中运行设备小时级时间尺度和分钟级时间尺度的协调优化,达到分级消除系统中随机和扰动因素的影响,实现高比例可再生能源的安全消纳.并且在多虚拟电厂优化调度模型中,借助Markowitz...     

基于系统辅助服务收益动态优化的多能联盟体市场策略

考虑风电或光伏能源发电波动性以及水电或火电能源对于电力系统辅助服务的支撑作用,以满足电力市场实时电量交易平衡和各主体最优效益为目标,基于多类能源联盟体运营机制,模拟各主体多尺度组合并构建辅助服务收益和出力比例关联的Shapley值动态分析模型,通过分析负荷峰、谷值运行时各主体最优发电比例和收益之间的变化规律,以此为各主体参与市场投标配置最优发电量和成本控制提供决策依据;同时论证了满足实时负荷需求的峰、谷值功率差额模式与辅助服务动态分配的耦合关系,该模式反映了辅助服务市场均衡组合变化趋势,并可从总体上平衡可再生能源波动性以减少实时市场交易增量对各主体收益的负面影响,且为各主体之间动态组合优化决策以及挖掘联盟体内部的资源价值奠定了基础。     

考虑源荷不确定性的分时电价动态修正机制研究

针对新型电力系统中可再生能源出力及负荷需求的不确定性造成源荷协调困难,导致难以制定合理的分时电价的问题,该文提出一种考虑源荷不确定性的分时电价动态修正机制。首先,根据可再生能源出力的波动性以及不确定性,建立新能源并网功率与并网电量偏差量化模型;其次,根据需求侧负荷的变化特征,结合可再生能源出力不确定性,通过多种不确定性因素影响条件的误差计算方法,建立电价概率密度模型。然后,根据负荷上报的用电量以及预报电价,建立考虑源荷不确定性的电力市场分时电价动态修正与优化模型,并采用粒子群算法进行模型求解。最后,通过实际运行数据仿真验证该文所提方法的有效性。     

基于多时空尺度辅助服务的多类能源协同运营优化机制研究

在可再生能源参与电力现货市场交易的环境下,考虑可再生能源发电波动性、系统节点电压稳定性以及实时负荷需求的影响,构建含有可再生能源的多类能源联盟体运营模式,模拟光伏、风电能源分别接入不同节点系统,以及选择系统典型的日负荷需求曲线(峰谷差率分别取55.68%、37.03%)设计不同的运行场景;并基于联盟体中各主体的均衡收益、收益均衡离散性评价、发电功率优化分配以及系统总收益构建多时空尺度辅助服务优化运营模型。研究表明:1)多类能源联盟体模式可有效减少可再生能源波动性对系统稳定性的影响,促进联盟体多个时段总体运营收益明显改善,充分挖掘系统辅助服务的市场价值;2)以满足负荷实时需求的多时空尺度辅助服务组合策略,可实现对于各主体多时段的出力优化以及市场投标报价决策的支持,以提升联盟体的市场竞争力;3)揭示了联盟体多时空尺度辅助服务总收益、各主体的均衡收益与负荷实时变化的关联规律和机理,为完善联盟体模式参与电力实时市场交易理论提供了实证数据支撑。     

Multi carrier energy systems and energy hubs: Comprehensive review,survey and recommendations

In this paper, the multi carrier energy (MCE) systems are reviewed from different point of views including mathematical models, integrated components and technologies, uncertainty management, planning objectives, environmental pollution, resilience, and robustness. The basic of MCE systems is formed by combination of cooling, heating and power (CCHP). The natural gas and electricity are the main inputs to MCE systems and the cooling, heating, and electricity are the common outputs. The regular energy converters in the MCE systems are combined heat and power (CHP), gas boiler, absorption-electrical chillers, power to gas (P2G) and fuel-cell. The generic energy storages are electrical, heating, cooling, hydrogen, carbon dioxide (CO₂) and hydro systems.     

Application of probabilistic wind power forecasting in electricity markets

This paper discusses the potential use of probabilistic wind power forecasting in electricity markets, with focus on the scheduling and dispatch decisions of the system operator. We apply probabilistic kernel density forecasting with a quantile‐copula estimator to forecast the probability density function, from which forecasting quantiles and scenarios with temporal dependency of errors are derived. We show how the probabilistic forecasts can be used to schedule energy and operating reserves to accommodate the wind power forecast uncertainty. We simulate the operation of a two‐settlement electricity market with clearing of day‐ahead and real‐time markets for energy and operating reserves. At the day‐ahead stage, a deterministic point forecast is input to the commitment and dispatch procedure. Then a probabilistic forecast is used to adjust the commitment status of fast‐starting units closer to real time, on the basis of either dynamic operating reserves or stochastic unit commitment. Finally, the real‐time dispatch is based on the realized availability of wind power. To evaluate the model in a large‐scale real‐world setting, we take the power system in Illinois as a test case and compare different scheduling strategies. The results show better performance for dynamic compared with fixed operating reserve requirements. Furthermore, although there are differences in the detailed dispatch results, dynamic operating reserves and stochastic unit commitment give similar results in terms of cost. Overall, we find that probabilistic forecasts can contribute to improve the performance of the power system, both in terms of cost and reliability. Copyright © 2012 John Wiley & Sons, Ltd.     

Renewable electricity in Sweden: an analysis of policy and regulations

This study aims to analyse the developments in renewable energy policy making in Sweden. It assesses the energy policy context, changes in the choice of policy instruments, and provides explanations behind policy successes and failures. Swedish renewable energy policy has been developing in a context of uncertainty around nuclear issues. While there has been made a political decision to replace nuclear power with renewables, there is a lack of consensus about the pace of phasing out nuclear power due to perceived negative impacts on industrial competitiveness. Such uncertainty had an effect in the formulation of renewable energy policy. Biomass and wind power are the main options for renewable electricity production. Throughout 1990s, the combined effect of different policy instruments has stimulated the growth of these two renewable sources. Yet, both biomass and wind power are still a minor contributor in the total electricity generation. Lack of strong government commitment due to uncertainty around nuclear issues is a crucial factor. Short-term subsidies have been preferred rather than open-ended subsidy mechanisms, causing intervals without subsidies and interruption to development. Other factors are such as lack of incentives from the major electricity companies and administrative obstacles. The taxation system has been successful in fostering an expansion of biomass for heating but hindered a similar development in the electricity sector. The quota system adopted in 2003 is expected to create high demand on biomass but does not favour wind power. The renewable energy aims are unlikely to be changed. Yet, the future development of renewable energy policies especially for high-cost technologies will again depend strongly on nuclear policies, which are still unstable and might affect the pace of renewable energy development.