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The change from a subsidized zonal pricing system to a full nodal pricing regime in the new Mexican electricity market could improve the efficiency of electricity system operation. However, resulting price modifications might also swing surplus across producers and consumers. In this paper, we calculate nodal prices for the Mexican power system and further analyze how allocations of financial transmission rights (FTRs) can be used to mitigate resulting distributional effects. The share of FTRs to be allocated to different generation plants and loads is studied as a second step of an electricity tariff subsidy reform agenda that includes, as a first step, the change to nodal pricing and, as a third step, the reformulation of actual regressive subsidies in a progressive way. We test our model in a realistic nodal price setting, based on an hourly modeling of the Mexican power system. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms that drive extreme negative and positive prices in day-ahead electricity prices is crucial for managing risk and market design. In this paper, we consider the problem of understanding how fundamental drivers impact the probability of extreme price occurrences in the German day-ahead electricity market. We develop models using fundamental variables to predict the probability of extreme prices. The dynamics of negative prices and positive price spikes differ greatly. Positive spikes are related to high demand, low supply and high prices the previous days, and mainly occur during the morning and afternoon peak hours. Negative prices occur mainly during the night and are closely related to low demand combined with high wind production levels. Furthermore, we do a closer analysis of how renewable energy sources, hereby photovoltaic and wind power, impact the probability of negative prices and positive spikes. The models confirm that extremely high and negative prices have different drivers, and that wind power is particularly important in relation to negative price occurrences. The models capture the main drivers of both positive and negative extreme price occurrences and perform well with respect to accurately forecasting the probability with high levels of confidence. Our results suggest that probability models are well suited to aid in risk management for market participants in day-ahead electricity markets. [ABSTRACT FROM AUTHOR]

Using a unique and highly detailed data set on energy consumption at the appliance-level for 200 Swedish households, seemingly unrelated regression (SUR)-based end-use specific load curves are estimated. The estimated load curves are then used to explore possible restrictions on load shifting (e.g. the office hours schedule) as well as the cost implications of different load shift patterns. The cost implications of shifting load from "expensive" to "cheap" hours, using the Nord pool spot prices as a proxy for a dynamic price, are computed to be very small; roughly 2-4% reduction in total daily cost from shifting load up to five hours ahead, indicating small incentives for households (and retailers) to adopt dynamic pricing of electricity. [ABSTRACT FROM AUTHOR]

We investigate the potential for aggregations of residential thermostatically controlled loads (TCLs), such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control. We present two arbitrage approaches: 1) a benchmark that gives us an optimal policy but requires local computation or real-time communication and 2) an alternative based on a thermal energy storage model, which relies on less computation/communication infrastructure, but is suboptimal. We find that the alternative approach achieves around 60%–80% of the optimal wholesale energy cost savings. We use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in California's intraday energy market. We investigate six sites over four years and find that the savings range from $2–$37 per TCL per year, and depend upon outdoor temperature statistics and price volatility. [ABSTRACT FROM PUBLISHER]

This paper aims to evaluate and improve the usefulness of publicly available electricity market prices for real-time optimal dispatching (RTOD) of a privately owned energy storage system (ESS) in a competitive electricity market. The RTOD algorithm seeks to maximize the revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day-ahead market. The pre-dispatch prices, issued by the Ontario independent electricity system operator, and the corresponding ex-post hourly Ontario energy prices are employed as the forecast and the actual prices. A compressed-air ESS is sized and employed for evaluations due to its lower capital expenditure and its ability to be positively influenced by the availability of waste heat. First, the conventional RTOD algorithm is developed by formulating a mixed integer linear programming problem. It is demonstrated that the forecast inaccuracy of publicly available market prices significantly reduces the ESS revenue. Then, a new adaptive algorithm is proposed and evaluated which adapts the objective function of the optimization problem online based on historical market prices available before real-time. The outcomes reveal that the proposed adaptive RTOD can significantly increase the ESS revenue compared to the conventional algorithm as well as the back-casting method proposed in prior studies. [ABSTRACT FROM PUBLISHER]

Many empirical exercises estimating demand functions, whether in energy economics or other fields, are concerned with estimating dynamic effects of price and income changes over time. This paper first reviews a number of commonly used dynamic demand specifications to highlight the implausible a priori restrictions that they place on short and long-run elasticities. Such problems are easily avoided by adopting a general-to-specific modeling methodology. Second, it discusses functional forms and estimation issues for getting point estimates and associated standard errors for both short and long-run elasticities--key information that is missing from many published studies. Third, our proposed approach is illustrated using a dataset on Minnesota residential electricity demand. [ABSTRACT FROM AUTHOR]