A dynamic network analysis of spot electricity prices in the Australian national electricity market

This study applies dynamic network analysis to the power sector, examining the relationship between regional spot electricity prices in the Australian National Electricity Market (NEM). In particular, we employ principal component analysis and generate Granger causality networks to examine the degree of interconnectedness of the NEM in a time-varying setting. We find that the derived measures of interdependence can be related to actual market events such as price spikes, unexpected high demand for electricity, sudden increases in price volatility, rebidding of dominant generators, the temporary or permanent outage of major power stations, and upgrades and limitations in transmission capacity. In the analysed network, we find that stronger dependence is exhibited by regional markets that are linked through interconnectors, while the direction of Granger causality can be related to interregional trade. We further examine the usefulness of the derived measures for forecasting distributional characteristics of spot prices such as the maximum price, volatility, price spreads, or upcoming periods of price spikes. Our results suggest that the derived network measures have predictive power, albeit limited, for the behaviour of spot electricity prices in the NEM.     

Design of the incentive mechanism in electricity auction market based on the signaling game theory

At present, designing a proper bidding mechanism to decrease the generators' market power is considered to be one of the key approaches to deepen the reform of the electricity market. Based on the signaling game theory, the paper analyzes the main electricity bidding mechanisms in the electricity auction markets and considers the degree of information disturbance as an important factor for evaluating bidding mechanisms. Under the above studies, an incentive electricity bidding mechanism defined as the Generator Semi-randomized Matching (GSM) mechanism is proposed. In order to verify the new bidding mechanism, this paper uses the Swarm platform to develop a simulation model based on the multi-agents. In the simulation model, the generators and purchasers use the partly superior study strategy to adjust their price and their electricity quantity. Then, the paper examines a simulation experiment of the GSM bidding mechanism and compares it to a simulation of the High-Low Matching (HLM) bidding mechanism. According to the simulation results, several conclusions can be drawn when comparing the proposed GSM bidding mechanism to the equilibrium state of HLM: the clearing price decreases, the total transaction volume increases, the profits of electricity generators decreases, and the overall benefits of purchasers increases.     

Do producers apply a capacity cutting strategy to increase prices? The case of the England and Wales electricity market

Promoting competition among electricity producers is primarily targeted at ensuring fair electricity prices for consumers. Producers could, however, withhold part of production facilities (i.e., apply a capacity cutting strategy) and thereby push more expensive production facilities to satisfy demand for electricity. This behavior could lead to a higher price determined through a uniform price auction. Using the case of the England and Wales wholesale electricity market we empirically analyze whether producers indeed did apply a capacity cutting strategy. For this purpose we examine the bidding behavior of producers during high- and low-demand trading periods within a trading day. We find statistical evidence for the presence of capacity cutting by several producers, which is consistent with the regulatory authority's reports.     

Energy price forecasting - problems and proposals for such predictions

This paper discusses the value of price forecasting in the electricity market during bidding or hedging against volatility. When bidding in a pool system, the market participants are requested to express their bids in terms of prices and quantities. Since the bids are accepted in order of increasing price until the total demand is met, a company that is able to forecast the pool price can adjust its own price/production schedule depending on hourly pool prices and its own production costs. This paper also discusses the challenges of price forecasting and describes some of the proposed methods for meeting these challenges.     

Econometric analysis of 15-minute intraday electricity prices

The trading activity in the German intraday electricity market has increased significantly over the last years. This is partially due to an increasing share of renewable energy, wind and photovoltaic, which requires power generators to balance out the forecasting errors in their production. We investigate the bidding behaviour in the intraday market by looking at both last prices and continuous bidding, in the context of a reduced-form econometric analysis. A unique data set of 15-minute intraday prices and intraday-updated forecasts of wind and photovoltaic has been employed. Price bids are explained by prior information on renewables forecasts and demand/supply market-specific exogenous variables. We show that intraday prices adjust asymmetrically to both forecasting errors in renewables and to the volume of trades dependent on the threshold variable demand quote, which reflects the expected demand covered by the planned traditional capacity in the day-ahead market. The location of the threshold can be used by market participants to adjust their bids accordingly, given the latest updates in the wind and photovoltaic forecasting errors and the forecasts of the control area balances.     

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.     

Area price and demand response in a market with 25% wind power

Denmark, east and west of the Great Belt are bidding areas with separate hourly area prices for the Nord Pool power exchange, covering four Nordic countries and parts of Germany. The share of wind power has now increased to 25% on an annual basis in western Denmark. This has a significant impact not only on the electricity wholesale prices, but also on the development of the market. Hourly market data are available from the website of Danish TSO from 1999. In this paper these data are analysed for the period 2004–2010. Electricity generators and customers may respond to hourly price variations, which can improve market efficiency, and a welfare gain is obtained. An important limitation for demand response is events of several consecutive hours with extreme values. The analysis in this paper is a summary and update of some of the issues covered by the EU RESPOND project. It shows that extreme events were few, and the current infrastructure and market organisation have been able to handle the amount of wind power installed so far. This recommends that geographical bidding area for the wholesale electricity market reflects external transmission constraints caused by wind power.     

Empirical observations of bidding patterns in Australia's National Electricity Market

For more than a decade, electricity industries have been undergoing reform worldwide. However, there are various, sometimes contradictory, conclusions about the performance of these restructured electricity markets. Market performance depends largely on how each market participant responds to the market design — including market rules, market operational procedures, and information revelation. In this paper, we identify and examine the strategies adopted by generators in Australia's National Electricity Market, based on publicly available data for the period from May 1, 2002 to May 31, 2003. We try to understand and answer some basic questions like how generators respond collectively or individually to changes in market conditions (e.g. load changes) and why they behave in this way. The statistics calculated from the data show that wide variations in the frequency of strategic bidding and rebidding exist; that generators more frequently use capacity offers as a strategic tool than price offers; that large generating units are more likely to use capacity strategies to control market prices; and that generators are capable of responding to changes in market conditions.     

Electricity prices and generator behaviour in gross pool electricity markets

Electricity market liberalisation has become common practice internationally. The justification for this process has been to enhance competition in a market traditionally characterised by statutory monopolies in an attempt to reduce costs to end-users. This paper endeavours to see whether a pool market achieves this goal of increasing competition and reducing electricity prices. Here the electricity market is set up as a sealed bid second price auction. Theory predicts that such markets should result with firms bidding their marginal cost, thereby resulting in an efficient outcome and lower costs to consumers. The Irish electricity system with a gross pool market experiences among the highest electricity prices in Europe. Thus, we analyse the Irish pool system econometrically in order to test if the high electricity prices seen there are due to participants bidding outside of market rules or out of line with theory. Overall we do not find any evidence that the interaction between generator and the pool in the Irish electricity market is not efficient. Thus, the pool element of the market structure does not explain the high electricity prices experienced in Ireland.     

The market value of variable renewables: The effect of solar wind power variability on their relative price

This paper provides a comprehensive discussion of the market value of variable renewable energy (VRE). The inherent variability of wind speeds and solar radiation affects the price that VRE generators receive on the market (market value). During windy and sunny times the additional electricity supply reduces the prices. Because the drop is larger with more installed capacity, the market value of VRE falls with higher penetration rate. This study aims to develop a better understanding on how the market value with penetration, and how policies and prices affect the market value. Quantitative evidence is derived from a review of published studies, regression analysis of market data, and the calibrated model of the European electricity market EMMA. We find the value of wind power to fall from 110% of the average power price to 50–80% as wind penetration increases from zero to 30% of total electricity consumption. For solar power, similarly low value levels are reached already at 15% penetration. Hence, competitive large-scale renewable deployment will be more difficult to accomplish than as many anticipate.     

电力市场下光伏上网定价模型研究

针对电力市场环境下现行的光伏固定上网统一电价忽略了电力系统为应对其出力波动性而增加的热备用容量成本的问题,为了合理体现光伏发电的真实价值,提出光伏竞价上网的电价模型,根据历史数据进行未来日的出力预测并参与竞价,而采用该竞价模型所产生的部分费用反补给常规发电机组,可减少购电总成本。进而基于果蝇优化算法,运用Matlab软件对一个仅包含2个常规发电机组和1座光伏电站的电力系统进行优化求解与仿真,数值结果验证了本文模型的正确性和有效性。     

Short-term electricity price forecast based on the improved hybrid model

Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.     

Industrial electricity demand in New York State

We will discuss electricity demand in manufacturing industries in upstate New York. Empirical results are presented based on data obtained at the electric utility service area level for the years 1969–1981. The equations are based on a partial adjustment model including relevant input prices and industrial output by SIC group. The estimated coefficients have the expected signs and are for the most part, statistically significant. There is considerable variation in price and output elasticities among SIC groups and between service areas, which suggests that energy policy and industrial policy at the state level should be tailored to specific industries and perhaps to specific subregions within the state. Forecasts of industrial electricity demand are based on assumptions of prices and industrial output growth made by the utility companies themselves. A comparison of these forecasts with those made by the utility companies indicates that electricity demand forecasts are sensitive to the inclusion of alternative fuel prices and to the level of sectoral disaggregation.     

Capacity commitment and price volatility in a competitive electricity market

The long lead time required to add new capacity in the electricity generation industry means that daily demands are necessarily served by capacity already installed. However, in a competitive market, even if the installed capacity was designed to serve the projected demands, frequent surpluses and occasional full utilization inevitably lead to price volatility. This paper develops a two-stage model of the generation market in which capacity construction occurs in stage 1, before demand realization, and price determination occurs in stage 2, when the equilibrium price ensures that the realized demand does not exceed the installed capacity. We show that price volatility and price spikes are inevitable, and that while price capping can mitigate high and volatile prices, it causes unmet demands and reduction in system reliability. This paper accentuates the interdependence among generating capacity, price volatility and service reliability, a primary cause of concern in the debate on electricity market reform.     

Electric vehicles in imperfect electricity markets: The case of Germany

We use a game-theoretic model to analyze the impacts of a hypothetical fleet of plug-in electric vehicles on the imperfectly competitive German electricity market. Electric vehicles bring both additional demand and additional storage capacity to the market. We determine the effects on prices, welfare, and electricity generation for various cases with different players in charge of vehicle operations. Vehicle loading increases generator profits, but decreases consumer surplus in the power market. If excess vehicle batteries can be used for storage, welfare results are reversed: generating firms suffer from the price-smoothing effect of additional storage, whereas power consumers benefit despite increasing overall demand. Strategic players tend to under-utilize the storage capacity of the vehicle fleet, which may have negative welfare implications. In contrast, we find a market power-mitigating effect of electric vehicle recharging on oligopolistic generators. Overall, electric vehicles are unlikely to be a relevant source of market power in Germany in the foreseeable future.     

Electricity demand in Kazakhstan

Properties of electricity demand in transition economies have not been sufficiently well researched mostly due to data limitations. However, information on the properties of electricity demand is necessary for policy makers to evaluate effects of price changes on different consumers and obtain demand forecasts for capacity planning. This study estimates Kazakhstan's aggregate demand for electricity as well as electricity demand in the industrial, service, and residential sectors using regional data. Firstly, our results show that price elasticity of demand in all sectors is low. This fact suggests that there is considerable room for price increases necessary to finance generation and distribution system upgrading. Secondly, we find that income elasticity of demand in the aggregate and all sectoral models is less than unity. Of the three sectors, electricity demand in the residential sector has the lowest income elasticity. This result indicates that policy initiatives to secure affordability of electricity consumption to lower income residential consumers may be required. Finally, our forecast shows that electricity demand may grow at either 3% or 5% per year depending on rates of economic growth and government policy regarding price increases and promotion of efficiency. We find that planned supply increases would be sufficient to cover growing demand only if real electricity prices start to increase toward long-run cost-recovery levels and policy measures are implemented to maintain the current high growth of electricity efficiency.     

Price elasticities of residential electricity demand: Estimates from household panel data in Bangladesh

We collect a household level panel dataset to estimate the price elasticities of electricity demand for different types of urban households in Bangladesh. We use an instrumental variable estimation strategy which exploits exogenous variation in average electricity prices induced by a value-added-tax shock. The results indicate significant heterogeneity in price elasticities by electricity consumption levels. We conduct a number of simulations under alternative policy scenarios to illustrate how incorporating the heterogeneous nature of price elasticities into pricing policy can help decrease electricity demand-supply mismatch and inequality in electricity consumption. The results have important policy implications for developing countries aiming to address major energy issues by implementing tariff reforms.     

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.     

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.     

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.