Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty

In view of the intermittency and uncertainty associated with both the electricity production sector of restructured power system and their competitive markets, it is necessary to develop an appropriate risk managing scheme. So that it is desirable to trade-off between optimum utilization of intermittent generation resources (i.e. renewable energy resources), uncertain market prices and related risks in order to maximize participants' benefits and minimize the corresponding risks in the multi-product market environment. The main goal of this paper is to investigate risk management by introducing a novel multi-risk index to quantify expected downside risk (EDR) which is caused by both the wind power and market price uncertainties. Value-at-Risk (VaR) method is used to assess the mentioned risk issue by the proposed weighted EDR, so that an optimal trade-off between the profit and risk is made for the system operations. Also, the roulette wheel mechanism is employed for random market price scenario generation wherein the stochastic procedure is converted into its respective deterministic equivalents. Moreover, the autoregressive integrated moving average (ARIMA) model is employed to characterize the stochastic wind farm (WF) generation by predetermined mean level and standard deviation of wind behavior as well as temporal correlation. The problem is formulated as a mixed-integer stochastic framework for a hydro-wind power system scheduling and tested on a generation company (GENCO).     

考虑风电和光伏出力不确定性的日调度优化方法

文章采用一种改进的序列运算算法建立风电及光伏出力的不确定性综合模型,并将该模型引入调度优化问题中,建立了计及CO2排放费用及备用出力费用影响的考虑风电和光伏出力不确定性的日调度优化模型。采用机会约束理论处理含风电及光伏出力的不确定性约束,提出了一种离散粒子群与连续粒子群混合的智能算法求解该模型。最后,通过包含风电场和光伏电站的IEEE39节点算例,对所提模型以及算法进行了仿真分析,验证了数学模型和优化算法的环保性、经济性和有效性。     

Modelling and analysis of resource scheduling in restructured power systems considering wind energy uncertainty

Concerns over rapid transformation of global climate patterns resulted in wide deployment of renewable energy sources especially wind energy. However, intermittent nature of wind energy makes resource scheduling aspect of system operator more complex. The emerging deregulation policies have added to the complexity of scheduling problem. Therefore, treating and scheduling of wind energy on deterministic basis would result in non-optimal energy dispatch and increased operation cost over scheduling horizon. This paper focuses on optimal scheduling methodology of wind energy in day ahead market (DAM) considering expected costs due to wind uncertainty in real time (RT) spot market under deregulated market structure. This paper introduces various possible costs, namely, spot market compensation cost, additional reserve cost and rescheduling cost applicable to deviation in scheduled power from DAM to spot market. The costs can be derived from front-end information of DAM, historical and statistical estimate of RT market conditions. The effectiveness of proposed methodology is examined using IEEE 30 bus system with thermal and wind energy generators. In addition, sensitivity analysis is performed to examine the effect of incentive margin on DAM scheduling. Simulation results are presented, discussed and affirmed the effectiveness of proposed scheduling methodology.     

Optimal investment strategies in decentralized renewable power generation under uncertainty

This paper presents a method for evaluating investments in decentralized renewable power generation under price un certainty. The analysis is applicable for a client with an electricity load and a renewable resource that can be utilized for power generation. The investor has a deferrable opportunity to invest in one local power generating unit, with the objective to maximize the profits from the opportunity. Renewable electricity generation can serve local load when generation and load coincide in time, and surplus power can be exported to the grid. The problem is to find the price intervals and the capacity of the generator at which to invest. Results from a case with wind power generation for an office building suggests it is optimal to wait for higher prices than the net present value break-even price under price uncertainty, and that capacity choice can depend on the current market price and the price volatility. With low price volatility there can be more than one investment price interval for different units with intermediate waiting regions between them. High price volatility increases the value of the investment opportunity, and therefore makes it more attractive to postpone investment until larger units are profitable.     

Optimal dispatching model of active distribution network considering uncertainty of demand response

在当前主动配电网日益发展的背景下,需求响应作为可调控的重要资源被引入配电网系统中,与分布式电源、储能等设备的共同作用下,保证配电网系统的安全稳定经济运行。然而系统响应过程中的外部环境的不确定性、价格需求曲线的不确定性等多方面因素,使得需求响应具有较大的不确定性,基于此,本文构建了一种考虑需求响应不确定性的主动配电网调度优化模型,以配电网运行成本最小为目标函数,重点分析两种不同类型的需求响应：基于激励的需求响应和基于价格的需求响应,考虑其不确定性条件下对主动配电网日前调度成本的影响,通过分段线性化对模型进行线性化处理,最终采用修改的IEEE33节点算例分析验证本文模型的有效性。     

Oil price uncertainty and unemployment

In this study, we empirically search the effects of oil price uncertainty and oil price shocks on U.S. unemployment rate using a GARCH-in-mean VAR model for the period 1974:q2–2017:q4. Based on our findings, we show that oil price uncertainty significantly increases unemployment rate in the U.S. economy. Likewise, our impulse-response analysis affirms that a positive oil price shock increases unemployment while the response of unemployment to a negative oil price shock is negative. Moreover, we reveal that unemployment rate reacts to positive and negative shocks asymmetrically. More specifically, the response of unemployment to negative oil price shocks is negative and slighter in size. Besides, oil price uncertainty is found to magnify the rise in the U.S. unemployment rate. These findings are in keeping with the real options theory which reveals that the uncertainty about goods' prices leads firms to postpone or abandon their production and investment and they seem to be robust to the use of different real oil price measures.     

考虑多源负荷不确定性的电力系统安稳风险优化

针对多源负荷的波动性和不确定性影响电力系统安稳运行,导致系统运行可靠性降低等问题,文章提出了一种考虑多源负荷不确定性的电力系统安稳风险优化方法;建立了考虑多源负荷不确定性的电力系统元件停运概率模型;提出了考虑多源负荷不确定性的电力系统安稳风险评估方法、安稳风险评估指标及其评估流程,对多源负荷波动下的电力系统安稳风险进行了评估。以系统运行过程安稳风险最低、负荷削减总量最小和系统经济损失后果最小为目标,建立电力系统安全稳定风险优化模型,对电力系统安稳风险进行优化。文章以IEEE-39节点算例进行仿真,验证了所建立模型的有效性。     

计及综合需求响应和不确定性风险的电气热耦合型微电网协同优化

多能耦合型微电网是提高系统运行效率和促进风电消纳的重要手段,文章提出了计及综合需求响应和不确定性风险的电气热耦合型微电网协同优化模型。该模型在分析多种能源交互响应特性的基础上,基于价格的综合需求响应引导用户用能行为,平滑系统负荷曲线。另外,通过分析不确定性的随机特性和模糊特性,提出一种基于可信性理论的风险评估方法,定量分析了风力发电的不确定风险,用来权衡多能耦合型微电网运行的效益和风险。最后,利用进化捕食算法求解所提模型,仿真结果表明,所提模型提高了可再生能源利用率,增强了微电网对不确定风险的抵御能力,并降低系统运行费用。     

Cross-regional integrated energy system scheduling optimization model considering conditional value at risk

Integrated energy system is a very important way to improve energy efficiency. Based on the combined heating cooling and power system, combined with energy storage equipment, a cross-regional integrated energy system scheduling optimization problem is studied. An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region. Meanwhile, the value at risk (VaR) theory is introduced and the operating constraints of equipment in the integrated energy system fully considered, the integrated energy system scheduling model with VaR is established. The example shows that the model can realize multi-type electrical, heating, and cooling load optimized by schedule across regions under the premise of satisfying the balance of energy supply and demand, which can reduce the system operation cost. The sensitivity analysis of the minimum expected cost and the influencing factors of conditional VaR is carried out to verify the validity and feasibility of the proposed model.

• An integrated energy system scheduling optimization model is established that meets the requirements of electrical, heating, and cooling load under a variety of energy sources while both considering the interaction of electrical, heating, and cooling load between regions, and complementation of them within one region.
• By using the conditional value at risk theory to consider various types of the integrated energy system complements and evaluates the operational risk of the system under optimal operating conditions of the system.
• The total cost of system scheduling operation is proportional to the storage capacity, which is inversely proportional to the heat storage capacity and inversely proportional to the pipeline capacity within a certain interval.

     

考虑弃风限电风险的风火打捆外送多目标优化调度方法

针对由于风电不确定性而造成风火打捆外送方式下系统弃风限电风险问题,提出了一种考虑弃风限电风险的风火打捆外送多目标优化调度方法。首先,根据风电功率预测误差Beta分布模型,建立了风电实际可发电功率的概率分布模型,量化分析风电功率不确定性对风火打捆外送方式下的系统弃风限电风险影响机理;引入多目标优化理论,以风电外送消纳电量最大、系统综合弃风限电风险值最小为优化目标,以风电、火电出力计划为优化控制变量,以风电实际可发电功率为随机变量,构建了风火打捆外送多目标优化调度模型;引入概率序列理论,将不确定性优化模型转化为确定性模型求解。通过算例仿真,验证了所提方法的正确性和有效性。     

A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting

Recently, microgrids have attracted considerable attention as a high-quality and reliable source of electricity. In this work energy management in microgrids is addressed in light of economic and environmental restrictions through (a) development of an operational strategy for energy management in microgrids and (b) determination of type and capacity of distributed generation (DG) sources as well as the capacity of storage devices (SD) based on optimization. Net present value is used as an economic indicator for justification of investment in microgrids. The proposed NPV-based objective function accounts for the expenses including the initial investment costs, operational strategy costs, purchase of electricity from the utility, maintenance and operational costs, as well as revenues including those associated with reduction in non-delivered energy, the credit for reduction in levels of environmental pollution, and sales of electricity back to the utility. The optimal solution maximizing the objective function is obtained using a hybrid optimization method which combines the quadratic programming (QP) and the particle swarm optimization (PSO) algorithms to determine the optimum capacity of the sources as well as the appropriate operational strategy for the microgrid. The fuzzy set theory is employed to account for the uncertainties associated with electrical power price. Application of the proposed method under different operational scenarios serves to demonstrate the efficiency of the proposed scheme.     

Robust bidding and offering strategies of electricity retailer under multi-tariff pricing

In this paper, an electricity retailer seeks to determine selling price for end-user consumers under fixed pricing (FP), time-of-use pricing (TOU) and real-time pricing (RTP). Furthermore, in order to provide power exchange between the retailer and the power market, bidding and offering curves should be prepared to bid and offer to the day-ahead market. Therefore, this paper proposes a robust optimization approach (ROA) to obtain optimal bidding and offering strategies for the retailer. To achieve this, ROA is used for uncertainty modeling of power market prices in which the minimum and maximum limits of prices are considered for uncertainty modeling. Lower and upper bounds of price is consecutively subdivided into sequentially nested subintervals which allows formulating robust mixed-integer linear programming (RMIP) problem. The proposed RMIP model helps retailer to select a robust decision in the presence of market price uncertainty. Furthermore, the bidding and offering curves of the retailer are obtained from sufficient data through solving these problems. Meanwhile, the uncertainty of customers demand and variable climate condition are modeled based on stochastic programming. To validate the proposed robust optimization model, three case studies are evaluated and the results are compared.     

考虑需求响应的风光燃储集成虚拟电厂双层随机调度优化模型

为促进以风光为代表的分布式能源发电并网,文章集成了风电、光伏发电、燃气轮机、储能系统和激励型需求响应为虚拟电厂,引入两阶段优化理论克服风光不确定性,建立VPP双层随机调度优化模型。首先,介绍了VPP的基本结构,建立了电源出力模型和需求响应模型。然后,建立了虚拟电厂双层调度优化模型:在上层模型中,根据风电和光伏发电日前预测结果,制定日前调度计划;在下层模型中,根据WPP和PV的实际输出,修正日前调度计划,形成最终VPP调度方案。最后算例分析表明:所提模型能够衔接日前调度和时前调度,降低系统缺电惩罚成本,提升VPP运营收益。储能系统能够利用自身充放电特性配合VPP内部风电和光伏发电出力,有利于平缓系统净负荷曲线。PBDR能够引导用户侧配合VPP发电调度,削峰作用弱于IBDR,而填谷作用强于IBDR。同时引入ESSs和DR后,VPP运营收益达到最高,表明ESSs和DR具有协同优化效应。     

Energy price uncertainty, energy intensity and firm investment

This paper examines the effect of energy price uncertainty on firm-level investment. An error correction model of capital stock adjustment is estimated with data on U.S. manufacturing firms. Higher energy price uncertainty is found to make firms more cautious by reducing the responsiveness of investment to sales growth. The result is robust to consideration of energy intensity by industry. The effect is greater for high growth firms. It must be emphasized that the direct effect of uncertainty is not estimated. Conditional variance of energy price is obtained from a GARCH model. Findings suggest that stability in energy prices would be conducive to greater stability in firm-level investment.     

Optimal retailer bidding in a DA market - a new method considering risk and demand elasticity

This paper presents a new method to determine the optimal demand function for a retailer in power markets. It assumes that the retailer purchases the energy from either the day-ahead or the regulation market and sells it to the end users through fixed and/or real-time pricing contracts. The load is assumed to be price sensitive and the retailer to be price-taker. Through participation in the market and managing its risk, the retailer attempts to maximize its profit. The proposed method is tested on a typical power market.     

Optimal dynamic emergency reserve activation using spinning,hydro and demand-side reserves

This paper proposes an optimal dynamic reserve activation plan after the occurrence of an emergency situation (generator/transmission line outage, load increase or both). An optimal plan is developed to handle the emergency, using the coordinated action of fast and slow reserves, for secure operation with minimum overall cost. It considers the reserves supplied by the conventional thermal generators (spinning reserves), hydro power units and load demands (demand-side reserves). The optimal backing down of costly/fast reserves and bringing up of slow reserves in each sub-interval in an integrated manner is proposed. The proposed reserve activation approaches are solved using the genetic algorithm, and some of the simulation results are also compared using the Matlab optimization toolbox and the general algebraic modeling system (GAMS) software. The simulation studies are performed on the IEEE 30, 57 and 300 bus test systems. These results demonstrate the advantage of the proposed integrated/dynamic reserve activation plan over the conventional/sequential approach.     

Optimal offering and allocation policies for wind power in energy and reserve markets

Proliferation of wind power generation is increasingly making this power source an important asset in designs of energy and reserve markets. Intuitively, wind power producers will require the development of new offering strategies that maximize the expected profit in both energy and reserve markets while fulfilling the market rules and its operational limits. In this paper, we implement and exploit the controllability of the proportional control strategy. This strategy allows the splitting of potentially available wind power generation in energy and reserve markets. In addition, we take advantage of better forecast information from the different day‐ahead and balancing stages, allowing different shares of energy and reserve in both stages. Under these assumptions, different mathematical methods able to deal with the uncertain nature of wind power generation, namely, stochastic programming, with McCormick relaxation and piecewise linear decision rules are adapted and tested aiming to maximize the expected revenue for participating in both energy and reserve markets, while accounting for estimated balancing costs for failing to provide energy and reserve. A set of numerical examples, as well as a case study based on real data, allow the analysis and evaluation of the performance and behavior of such techniques. An important conclusion is that the use of the proposed approaches offers a degree of freedom in terms of minimizing balancing costs for the wind power producer strategically to participate in both energy and reserve markets. Copyright © 2017 John Wiley & Sons, Ltd.     

Oil price drivers,geopolitical uncertainty and oil exporters' currencies

Empirical relationships between crude oil prices and exchange rates of oil exporting countries tend to vary over time. I use econometric models of the Norwegian and Canadian nominal exchange rates to investigate whether such time-variation could reflect shifts in the key drivers of oil prices over time. Results suggest that demand- and supply-driven oil price increases strengthen these currencies to different extents. In contrast, heightened geopolitical uncertainty and associated oil price increases go together with a weakening of oil exporters' currencies. The latter result may explain coincidences of higher oil prices and a weakening of oil exporters' currencies.     

Oil price uncertainty and cash holdings: Evidence from China

This study investigates the impact of oil price uncertainty on the cash holdings of firms. Using a sample of the Chinese stock market for the period from 2007 to 2016, our empirical results show that the impact of oil price uncertainty on cash holdings exhibits an inverted U-shape. Cash holdings increase with oil price uncertainty, but after a point, this impact becomes negative. Moreover, we find that the effect of oil price uncertainty is mitigated as the market value of firms increases. Meanwhile, state-owned companies are less impacted by oil price uncertainty. The results are robust to various tests and alternative explanations. Our findings are consistent with real options theory and pecking order theory.     

Oil price uncertainty and U.S. employment growth

This paper studies the effects of oil price changes on U.S. aggregate and sectoral employment growth in the presence of time-varying oil price uncertainty. We estimate a bivariate GARCH-in-Mean VAR model using U.S. monthly data of oil prices and employment growth for the period 1974 m2:2018 m11. Based on the results, we show that an increase in oil prices reduces total employment growth and that in most private sectors, but the public sector is largely unaffected. The effects on employment growth in various ”hub” sectors are also different. Furthermore, employment growths at both aggregate and disaggregate levels respond asymmetrically to positive and negative oil price shocks, which could possibly be attributed to oil price uncertainty. This asymmetric impact is more evident when the model is estimated on the entire sample than on the 1970s sample, implying that the role of oil price uncertainty in accounting for variations in employment growth can differ over time. These findings underline the empirical relevance of oil price uncertainty for the U.S. labor market dynamics.