An economic welfare analysis of demand response in the PJM electricity market

We analyze the economic properties of the economic demand-response (DR) program in the PJM electricity market in the United States using DR market data. PJM's program provided subsidies to customers who reduced load in response to price signals. The program incorporated a “trigger point”, at a locational marginal price of $75/MWh, at or beyond which payments for load reduction included a subsidy payment. Particularly during peak hours, such a program saves money for the system, but the subsidies involved introduce distortions into the market. We simulate demand-side bidding into the PJM market, and compare the social welfare gains with the subsidies paid to price-responsive load using load and price data for year 2006. The largest economic effect is wealth transfers from generators to non price-responsive loads. Based on the incentive payment structure that was in effect through the end of 2007, we estimate that the social welfare gains exceed the distortions introduced by the subsidies. Lowering the trigger point increases the transfer from generators to consumers, but may result in the subsidy outweighing the social welfare gains due to load curtailment. We estimate that the socially optimal range for the incentive trigger point would be $66–77/MWh.     

Estimating the option value of a non-firm electricity tariff

We estimate the option value of a non-firm electricity tariff commonly used by a local distribution company (LDC) in its electricity demand response program. This option value captures the benefit that a LDC enjoys from not serving an end-use load during high-price hours in a wholesale electricity market. It is conservative in that it does not include the cost savings in meeting the LDC's resource adequacy requirement or deferring transmission and distribution (T&D) investments necessary for delivering reliable service. Illustrated by a Northern California example, our two-pronged approach entails (a) a set of summer monthly market price regressions to forecast daily spot price distributions that incorporate uncertainty in natural gas price and weather; and (b) a simulation exercise to quantify the tariff's value under a specific design. The results indicate that a non-firm service tariff can have varying option value estimates that are highly sensitive to the tariff's design, and that an incentive payment based on the option value alone is likely insufficient to attract customer participation in a non-firm service program.     

Estimating the value of wind energy using electricity locational marginal price

There is an increasing interest in adding renewables such as wind to electricity generation portfolios in larger amounts as one response to concern about atmospheric carbon emissions from our energy system and the resulting climate change. Most policies with the aim of promoting renewables (e.g., RPS, FIT) do not explicitly address siting issues, which for wind energy are currently approached as the intersection of wind resource, land control, and transmission factors. This work proposes the use of locational marginal price (LMP), the location and time specific cost of electricity on the wholesale market, to signal locations where generation can address electricity system insufficiency. After an examination of the spatial and temporal behavior of LMP in Michigan over the first two years of wholesale market operation, this work combines LMP with wind speed data to generate a value metric. High value sites in Michigan tend to be sites with higher wind speeds, with the bulk of value accruing in the fall and winter seasons.     

A comparative analysis of the value of pure and hybrid electricity storage

Significant natural gas and electricity price variation and volatility, especially during the past few years, raise questions about understanding the value drivers behind electricity storage. The impact of these drivers for pure storage (such as pumped hydroelectric storage) and compressed air energy storage (CAES) are different and in this paper we explore these differences in operation and net revenue over a variety of timescales. We also consider the arbitrage value that is attainable in practice and explain why simple forecasting techniques based on historical data will generally be less successful for CAES. The breakeven cost of storage and how this can depend on regulatory treatment of storage and market structure is also considered.     

Market and welfare effects of renewable portfolio standards in United States electricity markets

This study analyzes the market and welfare effects of the introduction of Renewable Portfolio Standards (RPS) while considering the empirically relevant (a) interaction of compliance with voluntary green power markets, (b) differences in consumer preferences, and (c) imperfect competition among electricity suppliers. The study accounts for both the supply and demand effects of RPS — i.e., increased costs and a higher consumer valuation for regular power. Our analysis shows that the regular power price always increases after the introduction of RPS, while the effect of RPS on the equilibrium price of green power, the quantities of regular and green power, the welfare of consumers, and suppliers' profits is case-specific and dependent on the relative magnitude of the cost and utility effects, the strength of consumer preference for green power, the suppliers' costs before RPS, the impact of RPS on green power costs, and the degree of competition among power suppliers. While the introduction of RPS aims at increasing the use of green energy in electricity production, our analysis shows that the introduction of the policy can end up reducing the total quantity of green power used. Intriguingly, this adverse policy impact will occur under seemingly optimal conditions for the green power sector; i.e., a high consumer valuation of green energy and/or low cost difference between the green power and its conventional counterpart. Finally, the analysis shows that the policy design can play a key role in determining the incidence of RPS, while the identification of the winners and losers of the policy can provide insights on the political economy of RPS and the positions held by different groups in policy negotiations.     

Comparing welfare effects of different regulation schemes: An application to the electricity distribution industry

We compare the welfare effects of different regulation schemes of electricity distribution utilities. The compared regulation schemes are Fixed Price regulation, Cost of Service regulation, Menu of Cost-Contingent Contracts and Simple Menu of Contracts. In our calculations we utilize the information of a firm’s potential to improve cost efficiency. The firm-specific cost information of Finnish electricity distribution utilities is obtained by using various Stochastic Frontier models. Our basic result is that welfare can be improved by changing the Cost of Service regulation scheme to the Menu of Contracts regulation. Welfare also increases in the case of Fixed Price regulation and Simple Menu of Contract regulation. There is however, a significant difference among regulation regimes on how this improved welfare is distributed to consumers and producers.     

The role of storage in a competitive electricity market and the effects of climate change

This paper uses a new model of a competitive electricity market to investigate the role of storage in markets dominated by hydro generation. Competition among generators leads to an endogenous shadow price of stored water, which facilitates the efficient intra-day and inter-season substitution of fuel. Overall welfare depends on storage capacity, the cost structure of non-hydro generators, and the characteristics of water inflows. If climate change reduces the long-run average level of inflows or leads to the introduction of a carbon tax then overall welfare will fall and the profitability of generators will rise. The welfare benefits from additional storage capacity will increase if climate change makes long-term inflows less predictable or leads to the introduction of a carbon tax. They will decrease if average inflows fall or the predictable seasonal cycle in inflows becomes less pronounced.     

Estimating the potential for electricity savings in households

Improving efficiency in the use of energy is an important goal for many nations since end-use energy efficiency can help to reduce CO₂ emissions. Furthermore, since the residential sector in industrialised countries requires around one third of the end-use electricity, it is important for policy makers to estimate the scope for electricity saving in households to reduce electricity consumption by using appropriate steering mechanisms. We estimate the level of technical efficiency in the use of electricity using data from a Swiss household survey. We find an average inefficiency in electricity use by Swiss households of around 20 to 25%. Bottom-up economic-engineering models estimate the potential in Switzerland to be around 15%. In this paper we use a sub-vector input distance frontier function based on economic foundations. Our estimates lie at the upper end of the electricity saving potential estimated by the afore-mentioned economic-engineering approach.     

A welfare analysis of electricity transmission planning in Germany

We analyze the electricity transmission planning process in Germany (Netzentwicklungsplan), which separates transmission expansion decisions from generation dispatch. We employ an economic modeling approach to analyze two different network planning settings. In the first setting, there is no trade-off between transmission network development and generation dispatch, as is currently the case in Germany. A second setting alternatively allows for such a trade-off, and thus represents a welfare superior way of transmission network planning. Applications with the two model variants are carried out for the German electricity system in 2035. The results illustrate overinvestment in transmission capacity and decreased welfare associated with the Netzentwicklungsplan.     

Estimating the price elasticity of US electricity demand

The use of a single electricity price has been seen as a major shortcoming of econometric models of residential electricity demand. It has been suggested that demand estimation should be based on the full rate schedule. This suggestion is evaluated here by examining demand estimates for 27 investor-owned US utilities over the period 1957–1972. The specification errors resulting from using an average revenue price are measured. It is found that statistically valid demand estimated do not necessarily require information on tariff schedules, but that state-wide aggregates of elasticity may often be inapplicable to individual utilities.     

Economic and environmental implications of different approaches to hedge against wind production uncertainty in two-settlement electricity markets: A PJM case study

This paper examines the economic and environmental outcomes of four two-settlement electricity market clearing designs. The first design corresponds to a Deterministic Market Clearing (DMC) similar to the mechanism currently used in organized wholesale electricity markets in the United States. The other three designs account for the day-ahead (DA) wind power production uncertainty into the DA market mechanisms either implicitly or explicitly. An Augmented Deterministic Market Clearing (ADMC) design introduces DA ramp-capability products. These products ensure adequate and ramp-feasible electricity generation capacity commitments in the DA stage to cope with the real-time realization of wind power generation. A Hybrid Deterministic Market clearing (HDMC) design augments ADMC by explicitly integrating a characterization of wind power production uncertainty into the residual unit commitment (RUC) process, which is run after the DA market is closed, using stochastic programming. The last design, referred to as stochastic market clearing (SMC), uses stochastic optimization to explicitly account for wind power production uncertainty in the DA market clearing mechanisms (i.e. DA unit commitment and economic dispatch).The four market clearing designs are assessed by simulating the electricity market operations of a test system and comparing their results in terms of operating costs, prices, costs and revenues of different types of producers, consumer's payments, integration of wind power, and air emissions. The test system has 12% of the capacity of PJM's fossil-fired power generation fleet, and uses data on coincident demand and wind power production from the Bonneville Power Administration (BPA) system during years 2010–2014. The simulations are performed hourly for a whole year.Results show that SMC is superior as its costs reductions are more than two times the improvements attained by ADMC and HDMC. Also, SMC results in electricity prices that are better aligned with operation costs, cuts the spread between the day-ahead and real-time prices by >40%, reduces out-of-market short-term revenue sufficiency payments by 58%, reduces CO₂ emissions by 3.52%, and decreases power plants' cycling. HDMC is a distant second-best market design. Relative to DMC, it achieves a reduction in total costs that is less than half the reduction achieved by SMC, a reduction in out-of-market payments that is 80% of the reduction attained by SMC, and an increase in wind power integration that is <10% the improvement obtained under SMC.     

Performance of the SAC-C satellite electricity storage system

The analysis of telemetry data of the Argentine electricity storage system SAC-C satellite is presented. Diagnostic indicators were established in order to evaluate the in-flight performance of the satellite nickel-hydrogen batteries. The state of charge of the batteries was related to the hydrogen pressure. A predictive analysis allowed us to detect early failure of the electricity storage system.     

Grid-connected renewables,storage and the UK electricity market

This article is mainly a counterpoint to an article by Swift-Hook in the journal of Renewable Energy titled “Grid-connected intermittent renewables are the last to be stored”. It also describes the four main distinct UK markets where electrical energy and services are traded, in order to provide a context for the discussion of renewable energy and energy storage in the UK electricity system. In Swift-Hook’s article it was argued that “grid-connected intermittent renewables like wind energy will never be stored unless nothing else is available” and that “storage is counter-productive for fuel saving”. We, however, find evidence that “grid-connected intermittent renewables” have been, and will continue to be stored when it suits the “UK market” to do so. Furthermore, Swift-Hook’s article neglects the potential wider benefits that storage offers to UK energy policy’s goals, in terms of reduced emissions (when used in conjunction with renewables) and enhanced security of supply.     

Estimating and decomposing productivity growth of the electricity generation industry in Malaysia: A stochastic frontier analysis

This study examines the total factor productivity (TFP) growth of the Malaysian electricity generation industry over the 1998 to 2005 period. The stochastic frontier analysis (SFA) approach is used to measure TFP change and decompose TFP growth into efficiency change and technical progress. We find that it achieved average annual TFP growth of 2.34%, with technical change contributing the most to the TFP growth over the eight year period. We hence hypothesise that the new power plants with their newer capital-embodied technologies commencing during the sample period are likely to be the main reason for this strong technical change. In addition, it is also noted that this estimate for the Malaysian electricity generation industry is larger than the estimate obtained for the electricity sector as a whole, where we obtain 1.34% per year for a comparable period.     

An analysis of the welfare and distributive implications of factors influencing household electricity consumption

The deep economic crisis and the sharp rise in electricity prices have reduced electricity demand by Spanish households. This paper aims to analyse the responsiveness of household electricity demand and the welfare effects related to both factors in the 2006–2012 period by applying a demand model estimated with the quantile regression method. The results show that the electricity consumption of medium-high income households is particularly responsive to price increases, whereas that of medium-low income households is more responsive to changes in income. The retail electricity price increases and the economic crisis have led to lower and steeper U-shape price elasticities of demand and higher and steeper N-shape income elasticities of demand. The joint impact of those two factors on the welfare of lower-income households is higher in relative terms (i.e., as a share of household income) than for other income groups. These results suggest that the economic crisis and increases in retail electricity prices have had detrimental welfare effects, especially on the lower-income segment of the population. They should be considered when financing climate and energy policies through the electricity bill and provide a rationale to take such support, which pushes the retail electricity price upwards, out of the electricity bill.     

Estimating co-pollutant benefits from climate change policies in the electricity sector: A regression approach

We use data from US power plants and a regression based approach to empirically estimate the marginal rate of co-pollutant emission reductions resulting from a mass-based carbon reduction policy for electricity producers. The standard approach to estimating co-pollutant reductions uses Linear Programming Models. These models require millions of input variables and constraints, resulting in long computational times and an opaque simulation process, while yielding only point estimates for key variables of interest. Our regression-based approach has far fewer data requirements, needs less computational resources, and produces estimates with confidence intervals that capture estimation uncertainty. Moreover, it is straightforward and transparent to implement and provides a larger range of potential outcomes for policy makers to consider. Our results indicate that a 1% decrease in electricity output from coal (gas) power plants would reduce SO₂ by 0.6% and NO_x by 0.8% (0.7%). These are not statistically significant different than estimates reported by the Environmental Protection Agency (EPA). We estimate that reducing electricity output enough to reduce CO₂ emissions by one ton yields health benefits of $15.33 from NO_x reductions and $59.64 from SO₂ reductions.     

Estimating the agricultural demand for electricity in the presence of measurement error in the data

This paper begins by discussing some of the problems frequently encountered in obtaining demand elasticity estimates. To these problems is added that associated with inaccuracy in the measurement of the dependent variable and one or more of the independent variables that impact upon the quantity demanded. Two diagnostics — the regression coefficient bounds and the bias correction factor — are introduced to assess the effect that such a measurement error has on the estimated coefficients of demand relationships. Use of these diagnostics aids in assessing the integrity of the estimates obtained. In considering the demand for electricity for irrigation and the demand for electricity for other (non-irrigation) uses by farmers in the USA, both the quantity demanded and the unit price data available for demand model estimation purposes contain measurement errors. The regression coefficient bounds diagnostic is used to indicate a range over which the true price responsiveness of farmers to changes in energy prices lies. The results suggest that each 1% increase (decrease) in the price of energy will result in between a 0·51 and 0·35% decrease (increase) in the quantity of electricity demanded for irrigation and between a 0·43 and 0·17% decrease (increase) in the quantity of electricity demanded for other uses. The bias correction factor is computed to evaluate the magnitude of the under-estimation of the responsiveness of the quantity of electricity demanded for irrigation and electricity for other uses to a change in the number of acres irrigated and the number of acres planted. For electricity for irrigation, the under-estimation was 14·1% while, for electricity for other uses, it was 13·9%.     

Estimating the willingness to pay for reliable electricity supply: A choice experiment study

This research examines households' willingness to pay (WTP) for an improved electricity service. Households' stated WTP is estimated using the choice experiment (CE) method. The data used in the estimations come from 350 in-person interviews conducted during the period 5–22 August 2008 in North Cyprus. Compensating variation (CV) estimates for a zero-outage scenario are calculated using the parameter estimates from the mixed logit (ML) model; these are 6.65 YTL (Turkish lira) per month (3.02 USD) for summer and 25.83 YTL per month (11.74 USD) for winter. In order to avoid the cost of outages, households are willing to incur a 3.6% and a 13.9% increase in their monthly electricity bill for summer and winter, respectively. The WTP per hour unserved is 0.28 YTL (0.13 USD) for summer, and 1.08 YTL (0.49 USD) for winter. A preliminary cost–benefit analysis indicates that the annualized economic benefits are approximately 42.7 million YTL (19.4 million USD) for the residential sector. This would justify an investment in additional generation capacity of approximately 268 MW, which is far more than that which is needed to eliminate the service reliability problem.     

The value of information in electricity investment games

In this paper we look at the assumptions behind a Cournot model of investment in electricity markets. We analyze how information influences investment, looking at the way common knowledge of marginal costs, expectations on the competitors’ marginal costs, expectations on the level and duration of demand, and conjectures on the others’ behavior, influence the value of a project. We expose how the results are highly dependent on the assumptions used, and how the investment Nash–Cournot game with perfect and complete information implies such a degree of coordination between players that the outcome of the game would be classified by any regulation law as collusive behavior. Furthermore, we introduce the concept of Nash Value of Complete Information. As an example we use a stylized model of investment in liberalized electricity markets.