Enhanced network effects and stochastic modelling in generation expansion planning: Insights from an insular power system

Electricity generation capacity expansion is driven by both economic and socio-political realities. Policy makers determine public infrastructural decisions, such as climate and renewable targets, and transmission infrastructure, and the optimal generation capacity expansion follows. Policy makers therefore require planning models that can determine the optimal generation capacity mix in the long run under various scenarios, including policy choices. This work presents a planning model based on linearised alternating current optimal power flow which determines optimal generation capacity expansion and operation, in a least-cost manner, given global and local technical constraints, as well as policy decisions. We apply the model to a test case of the island of Ireland, which has two weakly interconnected systems, high renewable generation targets and low storage and interconnection. We determine the optimal generation expansion and operation out to 2030 considering the effects of increased multi-area interconnection, existing fossil fuel generation phase-out and increased renewable generation targets and carbon prices. Our results find that costs and emissions are driven primarily by the decommissioning of old inefficient generation units. High renewable targets, on the other hand, render increased carbon prices relatively ineffective in reducing system emissions. Furthermore, high renewable generation targets crowd out low-carbon power generation options such as carbon capture and storage (CCS). The strategic north-south interconnection has little effect on renewable energy source installations required to achieve renewable power generation targets but does impact on security of supply and the congestion level across the island.     

Vertical separation of transmission control and market efficiency in the wholesale electricity market

This study examines how vertical separation of transmission control affects the wholesale market efficiency in the electric power industry. We analyze a unique regional electricity wholesale market in the U.S. where initially restructuring only occurred in the transmission sector. Following a commonly-used best dispatch model (Wolfram, 1999; Borenstein et al., 2002), we simulate competitive benchmark prices and compare with the best estimates available for actual prices to measure price-cost markups of the wholesale market. Empirical results demonstrate that the vertical separation of transmission control led to a significant increase in market markups in peak-load hours, documenting evidence of enhanced market power. Although we also find a reduction in the price-cost margin in low-demand hours, we reserve caution for this finding.     

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures, and (iii) statistical testing of the significance of one model’s outperformance of another.     

Considering the nuclear option: Hidden benefits and social costs of nuclear power in the U.S. since 1970

Although burning fossil fuels has environmental consequences, many countries have switched away from nuclear power in favor of fossil-fuel fired electricity production after incidents at Three Mile Island, Chernobyl, and Fukushima. This study estimates the substitution between nuclear and fossil-fuel fired electricity generation in the United States. Using an event-study framework, we leverage nuclear plant openings from 1970 to 1995 and forced nuclear plant outages from 1999 to 2014. Plant openings (nuclear outages) reduce (increase) monthly net coal-fired generation by approximately 200 GWh, implying a considerable reduction (increase) in emissions. We find that the substitution between nuclear and coal is not one-to-one, as has been assumed in prior literature. After establishing these stylized facts, we explore the potential underlying forces driving the observed substitution between coal and nuclear.     

One size may not fit all: Welfare benefits and cost reductions with optional differentiated household electricity rates

We consider optional time-of-use (TOU) pricing for residential consumers, offered by a publicly regulated electricity supplier, as an alternative to a single TOU or flat rate structure. An equilibrium model explores and quantifies the effects of such pricing on welfare, consumption, and production costs. The supplier offers to each household a menu of possible rate structures obtained by maximizing a collective welfare function subject to three restrictions: Pareto efficiency, incentive compatibility, sufficiency of supplier revenue to cover costs. Simulations based on realistic calibration of the model demonstrate that optional pricing can increase overall consumer welfare and reduce average cost.     

Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match

Team QUINKAN competed in the GEFCom2017 final match of hierarchical probabilistic load forecasting by adopting the quantile regression method using the R package quantreg. The weather stations were clustered into 11 groups, from which an optimal one was chosen for each load meter using the boosting method. The load meter records were cleaned and/or supplemented by various methods in order to secure robust quantile predictions. The variation in the regression formulas was kept as small as possible by introducing measures for suppressing prediction instability, although special formulas were employed for loading meters that were of an industrial nature. Several procedures were applied to help improve the accuracy, such as the smoothing of season transitions, coarse graining of the relative humidity, the use of load-oriented day-type definition, the averaging of weather data, and outlier removal.     

Intraday hedging with financial options: the case of electricity

As market intermediaries, electricity suppliers purchase electricity from the wholesale market or self-generate to deliver their customers. However, electricity suppliers are uncertain about how much electricity their residential customers will use at any hour of the day until they actually turn switches on. While demand uncertainty is a common feature of all commodity markets, suppliers generally rely on storage to manage it. Singularly, electricity suppliers are exposed to joint volumetric and price risk on an hourly basis given the physical attributes of electricity. In the literature on electricity markets, few articles compare the efficiency of forward contracts, options and physical assets (i.e. power plants) within intraday hourly hedging portfolios, whereas electricity markets are precisely hourly markets. We analyse portfolios made of forwards, options and/or power plants for specific hourly clusters (9 am, 12 pm, 18 pm, 9 pm) based on electricity market data from 2013 to 2015 from the integrated German–Austrian spot market. Through a VaR model, we prove that intraday hedging with forwards is structurally inefficient compared to financial options and physical assets, no matter the cluster hour. Moreover, our results demonstrate the contribution of ‘out of the money’ options for all hours within volatile spot markets.     

Electricity supply preferences in Europe: Evidence from subjective well-being data

We use survey data for 139,517 individuals in 25 European countries, 2002–2011, to estimate the relationship between subjective well-being (SWB) and production shares of various electricity generation technologies. The estimated relationships are taken to represent preference relationships over attributes of electricity supply systems (costs, safety, environmental friendliness, etc.). Controlling for a variety of individual and macro-level factors, we find that individuals’ SWB varies systematically and significantly with differences in the electricity mix across countries and across time. Among other results, we find that a greater share of solar & wind power relative to nuclear power and electricity from coal and oil is associated with greater SWB at all levels of income and that the implied preference for solar & wind power over nuclear power has risen drastically after the Fukushima nuclear accident.     

DEMAND,CUSTOMER BASE‐LINE AND DEMAND RESPONSE IN THE ELECTRICITY MARKET: A SURVEY

The increasing use of demand‐side management, as a tool to reliably meet electricity demands at peak time, has stimulated interest among researchers, consumers and producer organiza‐tions, managers, regulators and policymakers. This research reviews the growing literature on models which are used to study demand, customer base‐line (CBL) and demand response in the electricity market. After characterizing the general demand models, the CBL, based on which the demand response models are studied, is reviewed. Given the experience gained from the review and existing conditions, the study combines an appropriate model for each case for a possible application to the electricity market; moreover, it discusses the implications of the results. In the literature, these aspects are studied independently. The main contribution of this survey is attributed to the treatment of the three issues as sequentially interdependent. The review is expected to enhance the understanding of the demand, CBL and demand response in the electricity market and their relationships. The objective is conducted through a combination of demand and supply side managements in order to reduce demand through different demand response programs during peak times. This enables electricity suppliers to save costly electricity generation and at the same time reduce energy vulnerability.     

How effective are heuristic solutions for electricity planning in developing countries

Heuristic algorithms have been widely used to provide computationally feasible means of exploring the cost effective balance between grid versus off grid sources for universal electrification in developing countries. By definition in such algorithms however, global optimality is not guaranteed. We present a computationally intensive but globally optimal mixed integer non-linear programming (MINLP) model for electricity planning and use it in a Monte Carlo simulation procedure to test the relative performance of a widely used heuristic algorithm due to [28]. We show that the overall difference in cost is typically small suggesting that the heuristic algorithm is generally cost effective in many situations. However we find that the relative performance of the heuristic algorithm deteriorates with increasing degree of spatial dispersion of unelectrified settlements, as well as increasing spatial remoteness of the settlements from the grid network, suggesting that the effectiveness of the heuristic algorithm is context specific. Further, we find that allocation of off grid sources in the heuristic algorithm solution is often significantly greater than in the MINLP model suggesting that heuristic methods can overstate the role of off-grid solutions in certain situations.