云南风电项目枯平期发电量占比计算方法探讨

2020年9月30日,云南“8 + 3”风光项目落地,并出台配套的结构电价政策,即风电项目枯平期2 000 h以内采用燃煤发电上网基准电价,丰水期500 h以内采用集中交易撮合下限价格,超额部分执行竞争性电价,因此对云南结构电价下风电项目发电量评估方法提出了新的要求。基于该电价规则,以云南某个拟建设的风电项目为例,分析实测数据、中尺度数据的每年枯平期风速以及风功率密度、发电量占全年比值,对比采用不同机型计算枯平期发电量占比的变化;并采用长时间序列数据计算风电场枯平期发电量占比,与周边已建成风电场进行对比分析;提出云南结构电价下风电项目的枯平期发电量占比计算方法及项目评估建议。研究结果可为类似云南结构电价下的风电项目发电量评估提供依据。     

Dynamic model for market-based capacity investment decision considering stochastic characteristic of wind power

This paper proposes a decentralized market-based model for long-term capacity investment decisions in a liberalized electricity market with significant wind power generation. In such an environment, investment and construction decisions are based on price signal feedbacks and imperfect foresight of future conditions in electricity market. System dynamics concepts are used to model structural characteristics of power market such as, long-term firms’ behavior and relationships between variables, feedbacks and time delays. For conventional generation units, short-term price feedback for generation dispatching of forward market is implemented as well as long-term price expectation for profitability assessment in capacity investment. For wind power generation, a special framework is proposed in which generation firms are committed depending on the statistical nature of wind power. The method is based on the time series stochastic simulation process for prediction of wind speed using historical and probabilistic data. The auto-correlation nature of wind speed and the correlation with demand fluctuations are modeled appropriately. The Monte Carlo simulation technique is employed to assess the effect of demand growth rate and wind power uncertainties. Such a decision model enables the companies to find out the possible consequences of their different investment decisions. Different regulatory policies and market conditions can also be assessed by ISOs and regulators to check the performance of market rules. A case study is presented exhibiting the effectiveness of the proposed model for capacity expansion of electricity markets in which the market prices and the generation capacities are fluctuating due to uncertainty of wind power generation.     

Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany

Although variable renewable energy (VRE) technologies with zero marginal costs decrease electricity prices, the literature is inconclusive about how the resulting shift in the supply curves impacts price volatility. Because the flexibility to respond to high peak and low off-peak prices is crucial for demand-response applications and may compensate for the losses of conventional generators caused by lower average prices, there is a need to understand how the penetration of VRE affects volatility. In this paper, we build distributed lag models with Danish and German data to estimate the impact of VRE generation on electricity price volatility. We find that in Denmark wind power decreases the daily volatility of prices by flattening the hourly price profile, but in Germany it increases the volatility because it has a stronger impact on off-peak prices. Our analysis suggests that access to flexible generation capacity and wind power generation patterns contribute to these differing impacts. Meanwhile, solar power decreases price volatility in Germany. By contrast, the weekly volatility of prices increases in both areas due to the intermittency of VRE. Thus, policy measures for facilitating the integration of VRE should be tailored to such region-specific patterns.     

Causal modeling and inference for electricity markets

How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oil, gas and coal prices, with German wind power and Nordic water reservoir levels as exogenous variables, we estimate a causal model for the price dynamics, both for contemporaneous and lagged relationships. In contemporaneous time, Nordic and German electricity prices are interlinked through gas prices. In the long run, electricity prices and British gas prices adjust themselves to establish the equilibrium price level, since oil, coal, continental gas and EUR/USD are found to be weakly exogenous.     

Wind farm generation forecast and optimal maintenance schedule model

Wind farms must periodically take their turbines offline in order to perform scheduled maintenance repairs. Given that these interruptions impact energy generation and that under Power Purchase Agreements productions shortfalls must be replaced by energy purchases in the spot market, the optimal time to begin maintenance work in a wind farm is a function of both the expected wind speeds and electricity spot prices. In this article, we develop a model to determine the optimal maintenance schedule of a wind farm based on forecasted wind speeds and energy prices. We analyze a window of opportunity in the most likely period of the year and perform weekly updates of expected wind speeds and energy price forecasts. Wind speeds are forecasted with an ARMAX model, where monthly dummies are used as exogenous variables to capture the seasonality of wind speeds, while spot prices are simulated under a standard dual stochastic programing model. The decision to defer maintenance to a future date is modeled in a probabilistic model and also under the real options approach. We test these models with actual data from a wind farm in the Brazilian Northeast and provide comparisons with current practice in order to determine the benefits of the model. The results suggest that this model may provide advantages over a stopping decision that randomly chooses a week to begin maintenance within the opportunity window and is close to the optimal stopping date considering perfect information on future wind speeds and electricity prices.     

The large-scale integration of wind generation: Impacts on price,reliability and dispatchable conventional suppliers

This work examines the effects of large-scale integration of wind powered electricity generation in a deregulated energy-only market on loads (in terms of electricity prices and supply reliability) and dispatchable conventional power suppliers. Hourly models of wind generation time series, load and resultant residual demand are created. From these a non-chronological residual demand duration curve is developed that is combined with a probabilistic model of dispatchable conventional generator availability, a model of an energy-only market with a price cap, and a model of generator costs and dispatch behavior. A number of simulations are performed to evaluate the effect on electricity prices, overall reliability of supply, the ability of a dominant supplier acting strategically to profitably withhold supplies, and the fixed cost recovery of dispatchable conventional power suppliers at different levels of wind generation penetration. Medium and long term responses of the market and/or regulator in the long term are discussed.     

Impact of wind farm integration on electricity market prices

Wind generation is considered one of the most rapidly increasing resources among other distributed generation technologies. Recently, wind farms with considerable output power rating are installed. The variability of the wind output power, and the forecast inaccuracy could have an impact on electricity market prices. These issues have been addressed by developing a single auction market model to determine the close to real-time electricity market prices. The market-clearing price was determined by formulating an optimal power flow problem while considering different operational strategies. Inaccurate power prediction can result in either underestimated or overestimated market prices, which would lead to either savings to customers or additional revenue for generator suppliers.     

The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland

The North Atlantic Oscillation (NAO) is a large-scale atmospheric circulation pattern driving climate variability in north-western Europe. As the deployment of wind-powered generation expands on electricity networks across Europe, the impacts of the NAO on the electricity system will be amplified. This study assesses the impact of the NAO, via wind-power generation, on the electricity market considering thermal generation costs, wholesale electricity prices and wind generation subsidies. A Monte Carlo approach is used to model NAO phases and generate hourly wind speed time-series data, electricity demand and fuel input data. A least-cost unit commitment and economic dispatch model is used to simulate an island electricity system, modelled on the all-island Irish electricity system. The impact of the NAO obviously depends on the level of wind capacity within an electricity system. Our results indicate that on average a switch from negative to positive NAO phase can reduce thermal generation costs by up to 8%, reduce wholesale electricity prices by as much as €1.5/MWh, and increase wind power generators' revenue by 12%.     

Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach

The literature on renewable energy sources indicates that an increase of the intermittent wind and solar generation affects significantly the distribution of electricity prices. In this article, the influence of two types of renewable energy sources (wind and solar photo voltaic) on the level and variability of German electricity spot prices is analyzed. The quantile regression models are built to estimate the merit order effect for different quantiles of electricity prices. The results indicate that both types of renewable generations have a similar, negative impact on the price level, approximated by the price median. When the price volatility, measured by the inter-quantile range (IQR), is considered, the outcomes show that wind and solar influence prices differently. Conditional on the level of the total demand, the wind generation would either increase (when the demand is low) or decrease (when the demand is high) the IQR. Meanwhile, the increase of solar power stabilizes the price variance for moderate demand level. Thus, policy supporting the development and integration of RES should search for a balance between the wind and solar power.     

Impact of wind generation control strategies, penetration level and installation location on electricity market prices

Integration of wind facilities into power system grids have several impact on power system related issues including; transmission congestion, optimum power flow, system stability, power quality, system economics and load dispatch. Consequently, wind farm control strategy, location across the distribution network and its penetration level could have an impact on electricity market prices. This paper addresses these issues, by developing a single auction market model. An optimal power flow problem was formulated for determining the close to real time electricity market-clearing price and the total cost of generation. Simulation results, considering different operational cases, are presented to highlight the impact.     

The impact of wind power on electricity prices

This paper investigates the impact of wind power on electricity prices using a production cost model of the Independent System Operator – New England power system. Different scenarios in terms of wind penetration, wind forecasts, and wind curtailment are modeled in order to analyze the impact of wind power on electricity prices for different wind penetration levels and for different levels of wind power visibility and controllability. The analysis concludes that electricity price volatility increases even as electricity prices decrease with increasing wind penetration levels. The impact of wind power on price volatility is larger in the shorter term (5-min compared to hour-to-hour). The results presented show that over-forecasting wind power increases electricity prices while under-forecasting wind power reduces them. The modeling results also show that controlling wind power by allowing curtailment increases electricity prices, and for higher wind penetrations it also reduces their volatility.     

计及多能需求的综合能源服务商优化运行策略研究

随着分布式发电技术的不断成熟及发展,未来综合能源服务将是整合不同类型分布式发电并满足用户不同用能需求的有效途径.提出了一种含有多种分布式发电资源同时考虑多用能需求的综合能源服务商优化运行策略模型.首先建立了含有风电、光伏、燃气轮机、电储能、电热泵、辅助锅炉等分布式资源及电、热用能需求的园区综合能源系统优化调度模型;其次...     

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.     

Quantifying the impacts of national renewable electricity ambitions using a North–West European electricity market model

This work builds a comprehensive North–West European Electricity Market model for the year 2020 and uses it to quantify the impacts of ambitious national renewable electricity targets. The geographical coverage of the model comprises Germany, France, Belgium, Netherlands, Luxemburg, Great Britain and Ireland. The model simulates the electricity market operation for the entire region at half hourly resolution and produces results in terms of electricity prices, cross border flows, emissions and associated total system costs. The impact of two carbon prices is examined within the model. Results highlight the policy challenges that arise when individual Member States formulate renewable energy plans in isolation in the absence of integrated modelling of interconnected regions as cross border power flows play a more significant role in market dynamics especially in the presence of geographically dispersed variable renewable generation sources such as wind and solar. From a policy perspective results suggest that based on these national plans, congestion will be present on a number of key lines at long periods during the year.     

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。     

Economic analysis of electrical heating with heat storage using grid integrated wind power

针对冬季供热期我国北方地区因电网风电接纳能力不足导致的严重弃风问题,研究了电力市场背景下采用蓄热式电采暖提高电网风电消纳规模的经济性评估问题,考虑弃风电量、弃风电价等因素影响,建立了弃风蓄热式电采暖系统的经济性评估模型,分析了影响蓄热式电采暖系统经济性的关键因素。结合某清洁供暖示范工程进行算例分析,分析了协定弃风电价对系统经济性的影响,对所提出的经济性评估模型的有效性进行验证,为蓄热式电采暖可行性分析奠定了理论基础。     

High penetration wind generation impacts on spot prices in the Australian national electricity market

This paper explores wind power integration issues for the South Australian (SA) region of the Australian National Electricity Market (NEM) by assessing the interaction of regional wind generation, electricity demand and spot prices over 2 recent years of market operation. SA's wind energy penetration has recently surpassed 20% and it has only a limited interconnection with other regions of the NEM. As such, it represents an interesting example of high wind penetration in a gross wholesale pool market electricity industry. Our findings suggest that while electricity demand continues to have the greatest influence on spot prices in SA, wind generation levels have become a significant secondary influence, and there is an inverse relationship between wind generation and price. No clear relationship between wind generation and demand has been identified although some periods of extremely high demand may coincide with lower wind generation. Periods of high wind output are associated with generally lower market prices, and also appear to contribute to extreme negative price events. The results highlight the importance of electricity market and renewable policy design in facilitating economically efficient high wind penetrations.     

Short-term optimal wind power generation capacity in liberalized electricity markets

Mainly because of environmental concerns and fuel price uncertainties, considerable amounts of wind-based generation capacity are being added to some deregulated power systems. The rapid wind development registered in some countries has essentially been driven by strong subsidizing programs. Since wind investments are commonly isolated from market signals, installed wind capacity can be higher than optimal, leading to distortions of the power prices with a consequent loss of social welfare. In this work, the influence of wind generation on power prices in the framework of a liberalized electricity market has been assessed by means of stochastic simulation techniques. The developed methodology allows investigating the maximal wind capacity that would be profitably deployed if wind investments were subject to market conditions only. For this purpose, stochastic variables determining power prices are accurately modeled. A test system resembling the size and characteristics of the German power system has been selected for this study. The expected value of the optimal, short-term wind capacity is evaluated for a considerable number of random realizations of power prices. The impact of dispersing the wind capacity over statistical independent wind sites has also been evaluated. The simulation results reveal that fuel prices, installation and financing costs of wind investments are very influential parameters on the maximal wind capacity that might be accommodated in a market-based manner.     

Incentives of carbon dioxide regulation for investment in low-carbon electricity technologies in Texas

This paper compares the incentives a carbon dioxide emissions price creates for investment in low carbon dioxide-emitting technologies in the electricity sector. We consider the extent to which operational differences across generation technologies – particularly, nuclear, wind and solar photovoltaic – create differences in the incentives for new investment, which is measured by the operating profits of a potential entrant. First, astylized model of an electricity system demonstrates that the composition of the existing generation system may cause electricity prices to increase by different amounts over time when a carbon dioxide price is imposed. Differences in operation across technologies therefore translate to differences in the operating profits of a potential entrant. Then, a detailed simulation model is used to consider a hypothetical carbon dioxide price of $10–$50 per metric ton for the Electric Reliability Council of Texas (ERCOT) market. The simulations show that, for the range of prices considered, the increase in electricity prices is positively correlated with output from a typical wind unit, but the correlation is much weaker for nuclear and photovoltaic. Consequently, a carbon dioxide price creates much stronger investment incentives for wind than for nuclear or photovoltaic technologies in the Texas market.     

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.