浅谈电力网络信息系统安全

为了满足社会供电需求,电力企业开始对电力网络投入成本建设,以此来缓解用电压力。电力网络构建之后,企业面临的则是潜在的信息安全问题,这是计算机网络自身缺陷造成的不稳定因素,对网络信息系统的正常运行造成了潜在隐患。针对这一点,文章分析了电力网络信息系统诸多安全隐患,并提出了电力网络信息安全防范的先进技术、管理策略,旨在营造和谐的网络环境。     

Using weather ensemble predictions in electricity demand forecasting

Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.     

Demand for electricity in Nigeria: Some empirical findings

This paper presents the findings from an econometric model of Nigerian electricity demand. Electricity consumers are partitioned into three classes-namely. Residential, Commercial and Industrial. It shows that over the last two decades there has been tremendous increase in both the supply and demand for electric energy. Although supply has increased, it has not been able to keep pace with demand. Various explanatory variables that determine changes in demand are analysed for each of the three consuming sectors. The findings are as follows.(i) Per capital income, previous level of electricity consumption and urbanization are the most significant explanatory variables for the Residential sector's electricity consumption. The short and long-run income elasticities are found to be below unity, while whose of urbanization are above unity. The average price of electricity, though rightly signed, was found to be insignificant.(ii) As regards the commercial sector, the significant explanatory variables were previous level of electricity consumption, income, average price of electricity and urbanization. Urbanization was, however, found to be the most sensitive variable in respect to changes in the sector's electricity demand.(iii) Previous level of industrial electricity consumption and degree of urbanization were the main explanatory variables for changes in the Industrial sector's demand for electricity. Industrial output and income were not found to be significant variables in explaining changes in the Industrial sector's demand for electricity.     

Consumption patterns for electricity

A substantial body of literature in economics has focused on appropriate pricing under conditions in which demand varies through time and the product supplied cannot be stored. However, only limited attention has been paid to modeling or estimating consumer demand under these conditions. This paper deals with these issues in the context of the demand for electricity. A periodic cubic spline formulation is presented which bridges the gap between econometric and pure time series methods of estimating the path of demand through time.     

Environmental regulation, technological diversity, and the dynamics of technological change

Inducing technological progress is an important objective of environmental regulation. We investigate under which conditions regulation-induced technological progress pursues the best technological option. We analyze a setting with vertical and horizontal technological progress, cost uncertainty, time-limited patent protection, and a case that is typical for some emissi4on-intensive industries, like electricity generation or the chemical industry. Under taxes and standards, only the current least-cost technology is used and developed, implying a lock-in into a possibly inferior technology. Tradable permits yield slower progress but can facilitate the simultaneous development of technologies, rendering lock-ins less likely.     

A two-level electricity demand model : Evaluation of the connecticut time-of-day pricing test

A two-stage budgeting model is developed for electricity demand where comsumption in each period is treated as a different commodity. A relative household demand model is first estimated, a consistent price index for electricity is constructed, and then a total electricity consumption model is estimated. Economic procedures are derived which permit application of the model to both time-of-day price situations and also declining vlock price situatiions which result in non-linear budget sets. The model is applied to both types of situations- the data from the Connecticut time-of-day pricing test as well as data from the declining block rate situation of the prevoius year. The model is also tested in a forecasting application to time-of-day customers.     

Determinants of price mark‐up tolerance for green electricity – lessons for environmental marketing strategies from a study of residential electricity customers in Germany

This paper develops hypotheses on the effects of various attitudinal and perceptual variables as well as socio‐demographic characteristics of residential electricity customers on an individual's willingness to pay a mark‐up for electricity generated from renewable energy sources compared with the price due for electricity from conventional sources. The hypotheses are tested with data from a standardized telephone survey of 238 household electricity consumers in Germany. 53.4% of the participants are willing to pay a mark‐up for green electricity. 26.1% report a price tolerance equal to a 5–10% increase in their current electricity bill. Binary logistic and ordinal regression analyses indicate that price tolerance for green electricity is particularly influenced by attitudes (1) towards environmental issues and (2) towards one's current power supplier, (3) perceptions of the evaluation of green energy by an individual's social reference groups, (4) household size and (5) current electricity bill level. The findings are used to derive suggestions for energy related informational activities of public institutions, green marketing strategies of energy companies and future consumer research regarding demand for pro‐environmental goods. Copyright © 2009 John Wiley & Sons, Ltd and ERP Environment.     

Vertical structure and the risk of rent extraction in the electricity industry

This paper studies how competition and vertical structure jointly determine generating capacities, retail prices, and welfare in the electricity industry. Analyzing a model in which demand is uncertain and retailers must commit to retail prices before they buy electricity in the wholesale market, we show that welfare is highest if competition in generation and retailing is combined with vertical separation. Vertically integrated generators choose excessively high retail prices and capacities to avoid rent extraction in the wholesale market when their retail demand exceeds their capacity. Vertical separation eliminates the risk of rent extraction and yields lower retail prices.     

Forecasting electricity prices using bid data

Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market participants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.     

Case study: Targeting demand-side management for electricity transmission and distribution benefits

This paper shows how a vertically integrated utility develops a least-cost transmission and distribution (T&D) plan that considers demand-side management (DSM) to defer capacity expansion necessary for serving growing demand. The plan is the result of applying dynamic optimization techniques to a T&D planning area in the service territory of Pacific Gas and Electric Company (PG&E), the largest privately owned utility in the USA. In the case study area alone, DSM enables PG&E to reduce the present value of its planned investment in local T&D from $112.3 million to $77.3 million over a 20-year period.     

Malmquist productivity index for multi-output producers: An application to electricity generation plants

Different types of plants are used to generate electricity in the US: single-, multi-, and mixed-electricity plants. In this paper, we question the best type/design of plants for both renewable and non-renewable electricity. To do so, we suggest a new index that takes the form of a Malmquist productivity index. The specificity of our new index is that it offers the option to investigate the performances and the causes of the performance changes for each type of electricity separately; this is not possible when relying on more standard indexes. Moreover, our new index takes the links between the inputs and the outputs into account, and is nonparametric in nature. Using our index, we study the performances of more than 5000 plants for the period 2000–2012. Our findings reveal that single-electricity plants perform better for renewable electricity, while multi-electricity plants perform better for non-renewable electricity. This is coherent with the decreasing importance of multi-electricity plants in the US, and the increasing importance of single-electricity plants producing renewable electricity. Furthermore, our results do not suggest that combining renewable and non-renewable electricity generations within a plant improves the performance of the plants. Finally, we demonstrate that the reasons for the changes in performance are different for each type of electricity and plant.     

An investment planning model for the electricity sector in Nigeria

In this study the methodology of mixed integer programming is used to deal with investment resource allocation problems that involve economies of scale in the Nigerial economic planning environment. The specific application is to long-term investment decisions in the government-operated electric power supply industry (NEPA). The investment planning problem involves determining the optimal generating plant/transmission mix from among a set of fossil fuel and conventional hydro plants which will meet the demand for electricity in Nigeria over the next three decades at minimum cost. Economies of scale in plant construction makes the investment cost function to be non-linear. However the non-linearity is approximated by a fixed charge function. Alternative price assumptions are made about locally found fuel resources (oil, gas, coal and liguite) to calculate the energy cost of fossil fuel plants. Different investment strategies are then evaluated in terms of the present values of the cost of supplying electricity to meet the required demand.     

Electricity derivatives pricing with forward-looking information

In order to increase overall transparency on key operational information, power transmission system operators publish an increasing amount of fundamental data, including forecasts of electricity demand and available capacity. We employ a fundamental model for electricity prices which lends itself well to integrating such forecasts, while retaining ease of implementation and tractability to allow for analytic derivatives pricing formulae. In an extensive futures pricing study, the pricing performance of our model is shown to further improve based on the inclusion of electricity demand and capacity forecasts, thus confirming the general importance of forward-looking information for electricity derivatives pricing. However, we also find that the usefulness of integrating forecast data into the pricing approach is primarily limited to those periods during which electricity prices are highly sensitive to demand or available capacity, whereas the impact is less visible when fuel prices are the primary underlying driver to prices instead.     

Information and energy policy preferences: a survey experiment on public opinion about electricity pricing reform in rural India

A common argument for the lack of economic reform in developing countries is popular opposition. If current economic policies are dysfunctional, could information about alternatives sway the voters? We examine if a simple argument emphasizing the need to increase electricity prices for improved supply can change public opinion in the case of India’s power sector reforms. The evidence comes from a survey experiment in rural Uttar Pradesh, which is both India’s largest state and has one of the lowest levels of household electrification. As expected, people respond to information about the relationship between electricity pricing, capacity investment, and reliability of supply by increasing their support for higher prices. However, no corresponding increase is observed for privatization of electricity generation. For external validity, we analyze an existing national survey on electricity privatization conducted in 2004/2005, finding patterns that support our argument.     

The industrial and commercial demand for electricity under time-of-use pricing

In this paper we have developed and estimated the demand for electricity by an industrial (commercial) firm subject to time-of-use (TOU) pricing of electric power. In the application we use a quadratic production function and directly incorporate into the production process the restrictions that some inputs cannot vary over the day. We show that the TOU structure implies a unique set of parameter restrictions across the demand functions for inputs.     

Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?

Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same regression or neural network model is calibrated to the prices themselves. Here, considering two novel extensions of this concept to probabilistic forecasting, we find that (i) efficiently calibrated non-linear autoregressive with exogenous variables (NARX) networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions. However, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Finally, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in electricity price forecasting.     

Econometric analysis of residential time-of-use electricity pricing experiments

This paper adapts the two-stage neo-classical model of consumer behavior to the analysis of time-of-use pricing of electricity. Emphasis is placed upon the relationship between partial elasticities, which can be accurately estimated from the first stage, and total elasticities, which can be estimated only by using less reliable information to estimate the second stage. Three functional forms are implemented with data from the Wisconsin Pricing Experiment. Results indicate that (1) the CES and generalized Leontief functional forms are preferred, (2) price elasticities vary substantially with price, and (3) peak and off-peak electricity are partial substitutes but total complements.     

Modelling alternative residential peak-load electricity rate structures

We specify a model of consumer behavior that permits the simultaneous estimation of effects of time-of-use rates having a demand charge, differential energy charges, and any combination of such charges. The model employs a flexible functional form and thus does not impose restrictions on substitution possibilities among types of usage. We estimate the model using data from the Wisconsin Residential Electricity Pricing Experiment for eight system peak days. The most surprising finding of our empirical application is that the two types of time-of-use rates employed in the Wisconsin experiment have similar effects on patterns of household electricity usage.     

Forecasting electricity prices with expert,linear, and nonlinear models

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.     

Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.