Optimal operation of an integrated energy system including fossil fuel power generation, CO2 capture and wind

This study considers the optimization of operations for an integrated fossil-renewable energy system with CO₂ capture. The system treated consists of a coal-fired power station, a temperature-swing absorption CO₂ capture facility powered by a natural gas combustion turbine, and wind generation. System components are represented in a modular fashion using energy and mass balances. Optimization is applied to determine hourly system dispatch to maximize operating profit given energy prices and wind generation data. A CO₂ emission constraint, modeled after a California law, is enforced. Idealized and realistic scenarios are considered, along with several different system specifications. For a year of operation, simulated using available wind and energy price data, operating profit for optimized operation is shown to be approximately 20% greater than profit using a heuristic procedure. The benefit from optimization is positively correlated with electricity price variability and mean wind generation. The impact of different component specifications and different CO₂ absorption solvents on the optimal operation of the energy system is also assessed. In total, this study demonstrates that the effective operating cost of an integrated energy system operating under a CO₂ emission constraint can be substantially reduced via optimal flexible operation.     

Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage

In this paper, three practical operation strategies (24Optimal, 24Prognostic, and 24Hsitrocial) are compared to the optimum profit feasible for a PHES facility with a 360 MW pump, 300 MW turbine, and a 2 GWh storage utilising price arbitrage on 13 electricity spot markets. The results indicate that almost all (∼97%) of the profits can be obtained by a PHES facility when it is optimised using the 24Optimal strategy developed, which optimises the energy storage based on the day-ahead electricity prices. However, to maximise profits with the 24Optimal strategy, the day-ahead electricity prices must be the actual prices which the PHES facility is charged or the PHES operator must have very accurate price predictions. Otherwise, the predicted profit could be significantly reduced and even become a loss. Finally, using the 24Optimal strategy, the PHES profit can surpass the annual investment repayments required. However, over the 5-year period investigated (2005–2009) the annual profit from the PHES facility varied by more than 50% on five out of six electricity markets considered. Considering the 40-year lifetime of PHES, even with low investment costs, a low interest rate, and a suitable electricity market, PHES is a risky investment without a more predictable profit.     

考虑动力电池梯次利用的混合发电厂容量配置

大量动力电池被淘汰,将淘汰的电池用作电力储能是解决问题的一个有效方法。选用风光和淘汰的动力电池作为储能的混合发电厂为研究对象,以目标函数为年利润最大进行容量配置,提出梯次利用电池的购买价格为全寿命周期等出力成本的方法,电池模型采取同时考虑使用次数、使用时间和充放电深度,并在可用容量相同的条件下每年进行置换更新的改进配置模型。通过最小乘子法、最优保存法和自适应步长法改进萤火虫算法,应用改进算法寻找最优解。最后选用张家口的天气参数进行模拟分析,验证模型算法的正确性,并对梯次电池回收价格进行研究。     

A market‐oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage

In this paper, an adaptive dispatch strategy is presented to maximize the revenue for grid‐tied wind power plant coupled with a battery energy storage system (BESS). The proposed idea is mainly based on time‐varying market‐price thresholds, which are varied according to the proposed algorithm in an adaptive manner. The variable nature of wind power and market price signals leads to the idea of storing energy at low price periods and consequently selling it at high prices. In fact, the wind farm operators can take advantage of the price variability to earn additional income and to maximize the operational profit based on the choice of best price thresholds at each instant of time. This research study proposes an efficient strategy for intermittent power dispatch along with the optimal operation of a BESS in the presence of physical limits and constraints. The strategy is tested and validated with different BESSs, and the percentage improvement of income is calculated. The simulation results, based on actual wind farm and market‐price data, depict the proficiency of the proposed methodology over standard linear programming methods.     

Resource extraction with a carbon tax and regime switching prices: Exercising your options

This paper develops a model of a profit maximizing firm with the option to exploit a non-renewable resource, choosing the timing and pace of development. The resource price is modelled as a regime switching process, which is calibrated to oil futures prices. A Hamilton-Jacobi-Bellman equation is specified that describes the profit maximization decision of the firm. The model is applied to a problem of optimal investment in a typical oils sands in situ operation, and solved for critical levels of oil prices that would motivate a firm to make the large scale investment needed for oil sands extraction, as well as to operate, mothball or abandon the facility. Regime shifts can have an important effect on the optimal timing of investment and extraction. The paper examines the effect of several carbon tax schemes on optimal timing of construction, production and abandonment. A form of Green Paradox is identified.     

Joint price and volumetric risk in wind power trading: A copula approach

This paper examines the dependence between wind power production and electricity prices and discusses its implications for the pricing and the risk distributions associated with contracts that are exposed to joint price and volumetric risk. We propose a copula model for the joint behavior of prices and wind power production, which is estimated to data from the Danish power market. We find that the marginal behavior of the individual variables is best described by ARMA–GARCH models with non-Gaussian error distributions, and the preferred copula model is a time-varying Gaussian copula. As an application of our joint model, we consider the case of an energy trading company entering into longer-term agreements with wind power producers, where the fluctuating future wind power production is bought at a predetermined fixed price. We find that assuming independence between prices and wind power production leads to an underestimation of risk, as the profit distribution becomes left-skewed when the negative dependence that we find in the data is accounted for. By performing a simple static hedge in the forward market, we show that the risk can be significantly reduced. Furthermore, an out-of-sample study shows that the choice of copula influences the price of correlation risk, and that time-varying copulas are superior to the constant ones when comparing actual profits generated with different models.     

Private and social benefits of a pumped hydro energy storage with increasing amount of wind power

In this paper, we calculate the long-term profitability of a pumped hydro energy storage (PHES) plant that is planned to be built in an old mine. We model the optimal PHES operation for several scenarios with different wind power penetration levels. Our modelling approach first involves estimating wholesale electricity prices for the day-ahead, intraday and balancing market as a function of wind power penetration. The estimated price profiles are implemented in a dynamic programming model, where the PHES plant maximises its balancing market revenue given the optimal commitment in the day-ahead market. We show that increasing the wind penetration changes the optimal PHES operation and increases the PHES profits. Additionally, we quantify how the costs of wind power balancing are affected by the PHES investment. Policy implications are drawn based on the estimated private and social benefits from the investment.     

Investigating the impact of variable energy prices and renewable generation on the annualized cost of hydrogen

Hydrogen can be produced from onsite renewable energy, energy from the power grid, or a combination of both. While the cost of grid energy is driven by the energy prices, onsite renewable investments are driven by the levelized cost of renewable energy. Energy prices can widely vary in time and in different locations, potentially having a large impact on the annualized cost of hydrogen (ACOH). Renewable sources can also vary significantly for different locations and seasons. In this paper, we develop a semi-empirical model to find key ACOH locational drivers and trends. The model finds the optimal capacity of the onsite solar and wind generators and the electrolyzer, together with the optimal use of grid energy. We found that an optimal balance between onsite renewable investments and grid energy use is strongly driven by the type of energy price (fixed or variable) and location.     

基于煤耗特性的火电厂动态成本模型研究

在"厂网分开,竞价上网"的电力体制下,电能成本的分析对发电企业的生产运营已显得尤为重要。重点研究在"煤电联动"机制还没有完全实现、机组负荷不稳定的条件下,电能成本如何变化。在分析了电能成本构成的基础上,运用定性、定量相结合的方法给出了机组成本、利润函数模型,并以实际数据分析了机组负荷率、煤耗率及燃煤价格对成本利润的影响。最后以某300MW电厂为例,给出了机组实时成本利润曲线。对火电企业的运营有一定指导意义。     

Operational performance of energy storage as function of electricity prices for on-grid hybrid renewable energy system by optimized fuzzy logic controller

Realization of benefits from on-grid distributed generation based on renewable energy sources requires employment of energy storage to overcome the intermittency in power generation by such sources, while accounting for time-varying electricity prices. The objective of this study is to examine the effects of time-varying electricity prices on the performance of energy storage components for an on-grid hybrid renewable energy system (HRES) utilizing an optimized fuzzy logic controller (FLC). To achieve the objective, FLC membership functions are optimized for minimizing the operational cost of the HRES based on weekly and daily prediction of data for grid electricity price, electrical load, and environmental parameters, including wind speed, solar irradiation, and ambient temperature, using shuffled frog leap algorithm. FLC three inputs include (a) grid electricity price, (b) net power flow as the difference between energy produced and energy consumed, and (c) state of charge (SOC) of battery stack. It is confirmed that accounting for grid electricity price has considerable effects on the performance of energy storage components for operation of on-grid HRES, as the weekly and daily optimized FLCs result in less working hours for fuel cell and electrolyzer and less fluctuations in SOC of battery stack.     

考虑需求响应的各季节能量枢纽优化运行

综合能源系统中能量枢纽的优化运行有助于提高能源利用效率,是综合能源系统中研究热点之一。基于能量枢纽结构和运行机制,建立了通用型能量枢纽模型,以运行费用最低为目标函数,采用粒子群优化算法对能量枢纽进行优化运行研究。该方法有效地考虑了能源价格和不同季节负荷特性对能量枢纽运行的影响,并与以热定电和以电定热两种运行模式进行对比分析。算例仿真表明,能量枢纽的优化运行不仅更具有经济效益,而且还有利于电网安全稳定的运行。     

Uncertainty—an argument for more stringent energy conservation

Most energy conservation analyses assume various future paths for energy prices and other parameters and then proceed to calculate the optimal value of conservation investment under the specified assumptions. Sensitivity analysis is used to examine the amount which the calculations change as parameters are varied. In fact, the future is not known with certainty, and it is desirable to include uncertainty explicitly in the analysis. This paper approaches the uncertainty problem in a simple way but one which provides considerable insight. We assume that future energy price growth is characterized by a probability distribution, and calculate the optimal investment strategy for conservation investment given this uncertainty. The results are striking. Introduction of uncertainty leads to the conclusion that more conservation investment is desirable than would be made without uncertainty. The conclusion stems, in essence, from the observation that the upside risk to the consumer resulting from unexpectedly high energy prices is larger than the downside savings which would result from unexpectedly low energy prices. The policy conclusion is straightforward: All else being equal, if you think that future energy prices are uncertain, it pays off (for both the individual and the nation) to err on the side of “too much” rather than “too little” conservation.One set of results is expressed in terms of an effective growth rate of energy prices. An example is an electrically heated house located in Washington, D.C. If the most likely growth rate of energy prices is 5% per year, but there is a normally distributed uncertainty about this price growth of 5%/yr (i.e. it is equally likely that prices will grow by 10%/yr and by 0%/yr), then one should calculate the conservation investment as if the price growth rate is 6.8%/yr.A second price assumption used is a price jump at some time from an initial price to a new, higher, final price. Uncertainty is introduced in the time when the price jump occurs. The inclusion of uncertainty leads to the conclusion that the effective time of the price jump is sooner than the most likely time. This result implies that a higher investment in conservation is warranted than would be optimal absent uncertainty.Our model is simple and maybe applied directly by individual decision makers. From a societal point of view, there are many additional arguments that suggest that uncertainty leads to changes in optimal strategies. The essential results cannot be captured simply by carrying out sensitivity analyses on deterministic models. Changes in model structure are required. We believe the inclusion of uncertainty in energy modeling is an important area, and one which can lead to fruitful results for policy analysis. We also make a prediction about energy conservation investment in buildings: Total energy use is predicted to fall exponentially with total conservation investment.     

Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices

Compressed air energy storage (CAES) technologies can be used for load levelling in the electricity supply and are therefore often considered for future energy systems with a high share of fluctuating renewable energy sources, such as e.g. wind power. In such systems, CAES plants will often operate on electricity spot markets by storing energy when electricity prices are low and producing electricity when prices are high. In order to make a profit on such markets, CAES plant operators have to identify proper strategies to decide when to sell and when to buy electricity. This paper describes three independent computer-based methodologies which may be used for identifying the optimal operation strategy for a given CAES plant, on a given spot market and in a given year. The optimal strategy is identified as the one which provides the best business-economic net earnings for the plant. In practice, CAES plants will not be able to achieve such optimal operation, since the fluctuations of spot market prices in the coming hours and days are not known. Consequently, two simple practical strategies have been identified and compared to the results of the optimal strategy. This comparison shows that, in practice, a CAES plant can be expected to earn 80-90 per cent of the optimal earnings.     

基于动态运行策略的太阳能分布式供能系统设计运行联合优化

提出将光伏剩余电量按照可变比例分配给储能电池及市政电网的动态运行策略,建立基于该策略的并网太阳能分布式供能系统设计运行联合优化模型,在不同分时电价下基于遗传算法对模型寻优,并将动态运行策略与对照运行策略（剩余电量优先并网或优先分配储能电池）下的系统运行结果进行比较分析。以陕西某乡村典型民居建筑为例进行分析,结果表明：1）分时电价的峰谷价差较大时,动态运行策略可有效降低太阳能分布式供能系统成本;2）分时电价的峰谷价差对于动态运行策略下储能电池的容量配置具有较大影响：峰谷价差越大,储能电池的配置容量越大;3）光伏度电补贴对3种运行策略下的系统成本影响程度为：动态运行策略>策略B（剩余电量优先分配储能电池）>策略A（剩余电量优先并网）。     

A combined modeling approach for wind power feed-in and electricity spot prices

Wind power generation and its impacts on electricity prices has strongly increased in the EU. Therefore, appropriate mark-to-market evaluation of new investments in wind power and energy storage plants should consider the fluctuant generation of wind power and uncertain electricity prices, which are affected by wind power feed-in (WPF). To gain the input data for WPF and electricity prices, simulation models, such as econometric models, can serve as a data basis.This paper describes a combined modeling approach for the simulation of WPF series and electricity prices considering the impacts of WPF on prices based on an autoregressive approach. Thereby WPF series are firstly simulated for each hour of the year and integrated in the electricity price model to generate an hourly resolved price series for a year. The model results demonstrate that the WPF model delivers satisfying WPF series and that the extended electricity price model considering WPF leads to a significant improvement of the electricity price simulation compared to a model version without WPF effects. As the simulated series of WPF and electricity prices also contain the correlation between both series, market evaluation of wind power technologies can be accurately done based on these series.     

European CO2 prices and carbon capture investments

We assess the option to install a carbon capture and storage (CCS) unit in a coal-fired power plant operating in a carbon-constrained environment. We consider two sources of risk, namely the price of emission allowance and the price of the electricity output. First we analyse the performance of the EU market for CO₂ emission allowances. Specifically, we focus on the contracts maturing in the Kyoto Protocol's first commitment period (2008 to 2012) and calibrate the underlying parameters of the allowance price process. Then we refer to the Spanish wholesale electricity market and calibrate the parameters of the electricity price process.We use a two-dimensional binomial lattice to derive the optimal investment rule. In particular, we obtain the trigger allowance prices above which it is optimal to install the capture unit immediately. We further analyse the effect of changes in several variables on these critical prices, among them allowance price volatility and a hypothetical government subsidy.We conclude that, at current permit prices, immediate installation does not seem justified from a financial point of view. This need not be the case, though, if carbon market parameters change dramatically, carbon capture technology undergoes significant improvements, and/or a specific governmental policy to promote these units is adopted.     

Potential of trading wind power as regulation services in the California short-term electricity market

A short-term electricity market is usually composed of the energy market and ancillary service market. However, wind power is not allowed to be traded in ancillary service markets although it has been proven technically feasible to be regulation services. This paper aims to explore the market potential of trading wind power as regulation services in the California electricity market. A model for wind power trade in the day-ahead (DA) market is established considering the uncertainties of market prices and wind power. An optimal trading strategy for wind power producers is derived by using an analytical algorithm. Trading wind power as regulation is tested by using actual data and the impacts of market control on the market outcome are discussed. The results show that, based on the current framework of the California electricity market, wind power producers can earn much more money if they bid in the DA energy and regulation markets than if they only bid in the DA energy market. The results also show that the potential to enhance profit for wind power producers is larger in the regulation down market than in the regulation up market.     

Multicarrier energy systems: Optimization model based on real data and application to a case study

Multicarrier energy systems are increasingly used for a number of applications, among which the supply of electricity, heating, and cooling in buildings. The possibility of switching between different energy sources is a crucial advantage for the optimal fulfillment of the energy demand. The flexibility of these systems can benefit from the integration with smart grids, which have strong variations in time during their operation. The energy price is the parameter that is usually considered, but also the primary energy factor and the greenhouse gases emissions need to be accounted. This paper presents an application of an operational optimization method for a multicarrier energy system, based on real data–driven model and applied to different countries. The generation plant of a hospital is considered as case study, coping with multiple energy needs by relying on different conversion technologies. The optimal operation of the system shows a wide range of variability, depending on the chosen objective function, the hour of the day, the season, and the country. The results are affected mostly by the energy mix of the electricity supplied from the power grid, which has a direct influence on the primary energy consumption and the greenhouse gases emissions and an indirect influence on the electricity prices.     

A bilevel model for electricity retailers' participation in a demand response market environment

Demand response programmes are seen as one of the contributing solutions to the challenges posed to power systems by the large-scale integration of renewable power sources, mostly due to their intermittent and stochastic nature. Among demand response programmes, real-time pricing schemes for small consumers are believed to have significant potential for peak-shaving and load-shifting, thus relieving the power system while reducing costs and risk for energy retailers. This paper proposes a game theoretical model accounting for the Stackelberg relationship between retailers (leaders) and consumers (followers) in a dynamic price environment. Both players in the game solve an economic optimisation problem subject to stochasticity in prices, weather-related variables and must-serve load. The model allows the determination of the dynamic price-signal delivering maximum retailer profit, and the optimal load pattern for consumers under this pricing. The bilevel programme is reformulated as a single-level MILP, which can be solved using commercial off-the-shelf optimisation software. In an illustrative example, we simulate and compare the dynamic pricing scheme with fixed and time-of-use pricing. We find that the dynamic pricing scheme is the most effective in achieving load-shifting, thus reducing retailer costs for energy procurement and regulation in the wholesale market. Additionally, the redistribution of the saved costs between retailers and consumers is investigated, showing that real-time pricing is less convenient than fixed and time-of-use price for consumers. This implies that careful design of the retail market is needed. Finally, we carry out a sensitivity analysis to analyse the effect of different levels of consumer flexibility.     

Fuel cost uncertainty,capacity investment and price in a competitive electricity market

This paper studies the effect of natural-gas fuel cost uncertainty on capacity investment and price in a competitive electricity market. Our model has a two-stage decision process. In the first stage, an independent power producer (IPP) builds its optimal capacity, conditional on its perceived uncertainties in fuel cost and electricity demand. In the second stage, equilibrium prices and quantities are determined by IPPs competing in a Cournot market. Under the empirically reasonable assumption that per MWh fuel costs are log-normally distributed, we find that a profit-maximizing IPP increases its capacity in response to rising fuel cost volatility. Consequently, the expected profit of the IPP and expected consumer surplus increase with volatility, rejecting the hypothesis that rising fuel cost uncertainty tends to adversely affect producers and consumers. Expected consumer surplus further increases if the IPP hedges the fuel cost risk. However, the IPP's optimal strategy is not to do so. The policy implication of these results is that the government should not intervene to reduce the price volatility of a well-functioning spot market for natural gas, chiefly because such intervention can have the unintended consequence of discouraging generation investment, raising electricity prices, and harming consumers.