Wholesale Electricity Prices Research Articles

ACCESSING THE EFFECT OF RENEWABLES ON THE WHOLESALE POWER MARKET

The purpose of this paper is to examine how rising wind energy generation (in MWh) impact the wholesale power market's volatility (in SEK) across four bidding regions in Sweden. Prior investigations show that though the increase in electricity production from wind energy lowers the average day-ahead electricity wholesale prices, however, uncertainty and volatility of market price could rise due to wind energy's intermittent nature. This study results show that Swedish power market experiences higher price volatility in long-run frequency when the generation of wind electricity increases. The reason for this high and volatile electricity price might be found from inflexible baseload power generation. The paper further suggests that volatility in the Swedish power market could be increased due to ambitious renewable electricity target by the Swedish government. The analysis concludes by providing the evidence that further adjustment in regard to the energy and regulatory policies might foster the better integration of a higher share of renewables into the power system.Keywords: Renewable Electricity, Wind Energy, Electricity Market, Price Volatility, Regulatory PoliciesJEL Classification: Q470DOI: https://doi.org/10.32479/ijeep.10756

Assessing the performance of deep learning models for multivariate probabilistic energy forecasting

Deep learning models have the potential to advance the short-term decision-making of electricity market participants and system operators by capturing the complex dependences and uncertainties of power system operation. Currently, however, the adoption of global deep learning models for multivariate energy forecasting in power systems is far behind the developments in the deep learning research field. In this context, the objectives of this study are to review recent developments in the field of probabilistic, multivariate, and multihorizon time series forecasting and empirically evaluate the performance of novel global deep learning models for forecasting wind and solar generation, electricity load, and wholesale electricity price for intraday and day-ahead time horizons. Two forecast types, deterministic and probabilistic forecasts, are studied. The evaluation data consist of real-world datasets with hourly resolution at the levels of an individual customer and regional and national electricity market bidding zones. The model evaluation criteria include achievable levels of forecasting accuracy and uncertainty risks, hyperparameter sensitivity, the effect of exogenous variables and fieldwise dataset split, and run-time efficiency factors, such as memory utilization, simulation time, electricity consumption, and convergence rate. We conclude that the performance of the global models is more beneficial for intraday forecasts of heterogeneous datasets with nonuniform patterns of time series, but can be affected by the hyperparameter sensitivity and hardware limitations with the growth of dataset dimensionality. The results can serve as a reference point for the quantitative evaluation of deep learning models for probabilistic multivariate energy forecasting in power systems.

Overcoming the limitations of variable renewable production subsidies as a means of decarbonising electricity markets

The limitations of production subsidies for variable renewable energy (VRE) plant as a means of decarbonising electricity markets are well known. Despite this, production subsidies for VRE plant are the most prevalent form of policy intervention in Australia. In the absence of cost-effective storage, production subsidies for VRE plant can create incentives to produce electricity at times the system may not need it, thereby severing the link between the spot market and financial incentives to generate. Such production subsidies can also reduce the incentive for new VRE plant to participate in ‘firm’ forward derivative markets. In this paper, we examine two adjustments that could be made to production subsidies for VRE plant that would correct for these unintended consequences. Firstly, the quantum of the subsidy could be a function of the wholesale electricity price, electricity demand or emissions; and secondly, new generators would be ineligible to receive production subsidies without demonstrating that they are facilitating the supply of financial derivative contracts. The effect of both adjustments would be to increase the incentive of VRE plant to demand-follow; that is, make its output ‘firmer’.

Uticaj tržišnih cena električne energije na isplativost buduće reverzibilne hidroelektrane u Srbiji

Deregulation in the power sector, liberalization of the electricity market, tendency to connect markets across Europe, introduction of incentives for construction and use of renewable energy sources (RES) and - consequently - their increasing presence in electricity systems, have taken a decisive impact on wholesale electricity prices. In addition, the introduction of CO2 taxes has further increased overall electricity prices. On the other hand, it reduced the profits of fossil fuel thermal power plants (TPPs). Also, in order to meet the prescribed requirements in the field of environmental protection, TPPs must apply pollution prevention systems that are very expensive, both in terms of investment and exploitation. As an alternative, investments in the hydro sector should be considered. However, this option is characterized by other issues; In contrast to the long-term construction and exploitation of hydro capacity, there are fluctuations in electricity prices on the day-ahead market which affect the work schedule and revenues of these facilities. To investigate this problem, hourly and daily prices in the regional electricity market were analysed. Trends in wholesale electricity prices on the day-ahead market have been determined. Their relevant values were estimated and used as one of the sets of input data for cost-benefit analysis and sensitivity analysis of the case study - future reversible hydropower plant (RHPP) "Bistrica". The impact of fluctuations in electricity prices on the day-ahead market was emphasized. The aim of this research was to draw a conclusion as to whether the movement of these prices is in favour of the construction of possible RHPPs in Serbia. Based on the conducted analyses, it can be concluded that the upward trend in the level of wholesale electricity prices (which began in 2019 and is expected to continue in the future considering the EU regulatory policy regarding CO2 emissions taxes) may positively affect the profitability of investment in RHE Bistrica (i.e. to shorten the payback period), but not so dramatically, since with the increase in electricity prices, the costs for pumping also increase.

Environmental Taxation and Subsidization in an Oligopolistic Wholesale Market of Electricity with Intermittent Renewable Energy Sources

I investigate how environmental taxation and subsidization impact investment in non-polluting, intermittent renewable power plants such as wind power and photovoltaics in a wholesale electricity market where oligopolistic firms produce electricity from both polluting fossil fuel inputs and non-polluting renewable energy (RE), and a competitive fringe produces electricity only from non-polluting RE. An emission tax imposed on fossil fuel use such as carbon pricing (CP) is expected to discourage firms from operating the fossil fuel power plants, thereby promoting the substitution of RE for fossil fuel inputs. A subsidy provided to firms building and operating renewable power plants such as feed-in tariffs (FIT) and feed-in premiums (FIP) is expected to reduce setup costs, thereby promoting RE use. This study’s findings indicate that, among FIT, FIP, and CP, a unit increase in subsidy rate under FIT has the greatest impact on RE investment, while a unit increase in tax rate under CP has the smallest impact. Furthermore, if no correlation exists between available renewable power plants at different locations, the wholesale electricity price in an oligopolistic market with FIP or CP is less volatile than that with FIT. This result occurs because the market power of oligopolists facing FIP or CP mitigates the merit order effect, which indicates how much RE lowers the electricity price. The key insight from the model application to a wholesale electricity market in Japan is that the market demand function for wholesale electricity and RE investment costs are among the crucial factors in the achievement of welfare-optimal allocation through environmental taxation and subsidization.

Identifying consumption profiles and implicit cross-subsidies under fixed-rate electricity tariffs

Wholesale electricity prices can rapidly change in real-time, yet households usually face fixed-price electricity tariffs. Therefore, households that predominantly use energy when wholesale prices are low implicitly cross-subsidize households whose energy use is more weighted to high-price periods. We develop a decomposition method that maps substation data on electricity use to demographic data, identifying the household characteristics associated with this cross-subsidization in Victoria, Australia. We find that households in areas with low house prices and high levels of renters and elderly residents are the net funders of this implicit subsidy. These households currently have the lowest average energy cost for retailers to service, and may be the greatest immediate beneficiaries if real-time retail tariffs are made available to them. Finally, we present evidence that cross-subsidy magnitudes have been growing in recent years, coincident with rapid solar generator penetration.

Cross-country Spillovers of Renewable Energy Promotion – The Case of Germany

Electricity generation based on renewable energy (RE) sources such as wind and solar replace the most expensive generators that often rely on fossil fuels. In response to RE promotion, wholesale electricity prices and carbon emissions are therefore expected to decrease. In interconnected electricity systems, this so-called merit- order effect stimulates a change in electricity trade ows. Therefore, conventional generation and prices in neighboring countries are also likely to decrease. The impact of these trade reactions on carbon offsets is ambiguous and depends on installed generation and interconnector capacities. Moreover, the cross-border merit-order effect causes opposing effects on consumers and producers: Generators' profits decline, while consumers benefit from lower electricity costs and an increase in the consumer surplus. Using a rich data set of hourly technology-specific generation and wholesale market price data for ten central European countries, we estimate the domestic and cross-border impacts of German RE for the years 2015 to 2020. We find that German RE generation offset 79 to 113 MtCO2 per year. The major emission effect took place in Germany (64 - 99 MtCO2). The average cost of emission offset of 212 to 321e/t were almost entirely borne by German market participants. Neighboring countries do not bear costs, but a significant shift from producer to consumer rents is observed.

Wind Power Revenue Potential: Simulation for Finland

Potential revenue from wind power generation is an important factor to be considered when planning a wind power investment. In the future, that may become even more important because it is known that wind power generation tends to push electricity wholesale prices lower. Consequently, it is possible that if a region has plenty of installed wind power capacity, revenue per generated unit of electricity is lower there than could be assumed by looking at the mean electricity wholesale price. In this paper, we compare 17 different locations in Finland in terms of revenue from wind power generation. That is done by simulating hourly generation with three different turbine types at two different hub heights and multiplying that by the hourly electricity spot price for years 2018 and 2019. Estimated revenues differ greatly between locations and turbine types, major factor being technical potential i.e., the amount of electricity generated. Differences between revenues per generated MWh seem to be small, however, the smallest figures being on the western coast where installed capacities are also the largest in Finland.

The impact of wind, solar, and other factors on the decline in wholesale power prices in the United States

Across multiple organized wholesale power markets in the United States, annual average prices declined by $19–64/MWh between 2008 and 2017 while retirements of thermal power plants accelerated. Several prominent changes over the last decade are often discussed as contributors to this decline in prices. These include growth in wind and solar, a reduction in the price of natural gas, and weakened load growth. Here we construct a fundamental supply curve model for each of seven organized wholesale market regions and use counterfactual simulations to assess the degree to which wind and solar—among other factors—have influenced wholesale electricity prices. We find that growth in wind and solar since 2008 reduced average annual wholesale electricity prices by less than $3/MWh. In contrast the decline in natural gas prices reduced wholesale prices by $7–53/MWh, depending on the region. This suggests that recent thermal-plant retirements in the U.S. are primarily due to low natural gas prices, not growth in wind and solar. Fully isolating the impact of individual factors, however, is limited by non-linear interactions between factors.

A time-of-use pricing strategy for managing electric vehicle clusters

Abstract A demand response program for electric vehicles (EV) is proposed to control the charging decision process in EV clusters. This approach corresponds to a time-of-use solution which is an indirect method, based on prices, for inducing demand modifications on consumers. An aggregator of EV fleet acts as a dealer between an electricity market and consumers. The EV aggregator is a price-taker agent from the wholesale electricity market viewpoint and price designer when selling energy to consumers. A game-theoretical model based on a Stackelberg formulation is proposed to capture the interactions between the fleet operator and electric vehicle owners, avoiding the requirement of a price elasticity model for the EV clusters. The interaction between the agents is formulated as a bi-level optimization problem: At the upper-level, the aggregator maximizes its benefits whereas the lower-level represents the dynamic behaviour of rational drivers as a fleet. The EV operator faces uncertainty in wholesale prices when buying energy and when forecasting consumption behaviour, then random parameters are modelled in a scenario framework. The model performance is evaluated through a case study using historical data from car-sharing services in Italy, comparing the result with a fixed-prices model. It is shown that the proposed price-based scheme allows to increment the aggregator profit with respect to a fixed-price contract, producing also a load shifting effect in the charging profile of the fleet.

Who benefits from ratepayer-funded auctions of transmission congestion contracts? Evidence from New York

Transmission congestion contracts (TCCs) are derivative products that electricity retailers and generators can use to change their future wholesale electricity price exposure to a different location. Network auctions of TCCs can help facilitate efficient contracting between participants located at different locations by signaling feasible least-cost configurations of electricity injections and withdrawals in the transmission constrained network. Federal and State regulators are concerned that market participants are profiting from these auctions at the expense of ratepayers. I study firm-level TCC positions in the New York Wholesale Electricity Market to investigate potential distributional and economic efficiency consequences from the auctions. I find no evidence that generator owners profit from TCC positions by leveraging their downstream market power in the wholesale electricity market. Instead, profitable actions by a small set of experienced financial traders tend to provide improved information and liquidity to the market. Policy implications and alternate regulatory frameworks are discussed.

Impacts of LNG Export and Market Power on Australian Electricity Market Dynamics, 2016–2019

Purpose of ReviewSupply-side shocks in concentrated energy markets provide opportunity for exercise of market power, especially in markets undergoing transition due to imperatives such as decarbonisation. In Australia, the recent linkage of international gas markets with the electricity market provides a useful example to review these dynamics.Recent FindingsUsing the intersections between gas and electricity markets in Australia, we explore how (1) supply constraints associated with commencement of Liquefied Natural Gas (LNG) exports, and (2) market concentration due to closure of old coal plant, contributed to effect a doubling of wholesale electricity prices. The recent Australian experience highlights the disproportionate impact of gas generation on electricity prices due to its price-setting role. The price impact of the tightened gas market on electricity prices has been significantly exacerbated through the exercise of market power, especially via the practise of ‘shadow pricing’. In the Australian case, the potential adverse intersectional impacts of LNG exports could have been substantially adverted with enforced domestic reservation of about 6% of LNG export volumes.SummaryCompetition issues and gaming opportunities, which have accompanied the increased market concentration that accompanied the withdrawal of capacity, have exacerbated the price impacts of the supply-side shock stemming from the evolving gas market.

High-performance charging for the electrification of highway traffic: Optimal operation, infrastructure requirements and economic viability

Battery electric vehicles are rapidly entering the market. Their success offers great opportunities for the decarbonization of the transport sector, but also pose new challenges to energy infrastructures. Public charging stations must be built and power grids may become congested. In this article, we analyze the optimal layout and operation of charging systems along highways. We introduce an optimization model that resolves every single charging event based on actual high-resolution demand profiles, thus providing a detailed account of infrastructure operation and utilization. We discuss the economic viability and identify potential roadblocks impeding a rapid build-up of electric mobility.We find that the installation of high-performance charging stations is highly profitable even for rather low demand, mainly due to the spread between wholesale and retail electricity prices. Congestion of regional distribution grids becomes a serious issue already for a moderate market penetration of electric vehicles. While peak loads can be handled by battery electric storage systems, the grid connection fundamentally limits the total amount of cars that can be served per day. Given the long time period needed for the planning and realization of infrastructure measures, rapid decisions are needed to enable the deep decarbonization of the transport sector.

Integration of Joint Power-Heat Flexibility of Oil Refinery Industries to Uncertain Energy Markets

This paper proposes a novel approach to optimize the main energy consumptions of heavy oil refining industries (ORI) in response to electricity price uncertainties. The whole industrial sub-processes of the ORI are modeled mathematically to investigate the joint power-heat flexibility potentials of the industry. To model the refinery processes, an input/output flow-based model is proposed for five main refining units. Moreover, the role of storage tanks capacity in the power system flexibility is investigated. To hedge against the electricity price uncertainty, an uncertain bound for the wholesale electricity price is addressed. To optimize the industrial processes, a dual robust mixed-integer quadratic program (R-MIQP) is adopted; therefore, the ORI’s operational strategies are determined under the worst-case realization of the electricity price uncertainty. Finally, the suggested approach is implemented in the south-west sector of the Iran Energy Market that suffers from a lack of electricity in hot days of summer. The simulation results confirm that the proposed framework ensures industrial demand flexibility to the external grids when a power shortage occurs. The approach not only provides demand flexibility to the power system, but also minimizes the operation cost of the industries.

The role of residential rooftop photovoltaic in long-term energy and climate scenarios

The use of solar photovoltaic has strongly increased in the last decade. A significant part of this growth comes from home owners installing rooftop photovoltaic. Despite this key role, most long-term model-based scenarios do not consider decentralized supply of rooftop photovoltaic but concentrate on utility-scale photovoltaic instead. In this paper, we implement rooftop photovoltaic in the Integrated Assessment Model IMAGE to study its possible role in energy and climate scenarios. We first calculated the global technical and economic potential to derive regional cost-supply curves for rooftop photovoltaic. Next, we have added a new decision in the IMAGE model allowing household investment in rooftop photovoltaic based on the comparison of the whole-sale electricity price with the price of rooftop photovoltaic. The global suitable roof surface area was assessed at 36 billion m2, or 4.7 m2 capita−1, leading to a potential for rooftop photovoltaic of 8.3 PWh y−1, roughly 1.5 times the 2015 global residential electricity demand. In the baseline scenario, adding rooftop photovoltaic could lead to a 80–280% increased share of photovoltaic electricity production in 2050 (i.e. from 6% to 17% in total power production). This increase depends on regional characteristics that are essential to the deployment of rooftop photovoltaic: differences in social-economic and policy factors (capital costs, household income, and electricity prices) are considerably more important than physical factors, such as solar irradiance.

Adaptive mechanisms to refund emissions payments

Carbon pricing is often discussed as a means by which to reduce power sector emissions, but typically faces opposition on the grounds that such schemes will increase wholesale electricity prices and have adverse impacts on the wider economy. Consequently, there is renewed focus on emissions pricing initiatives that have muted price impacts, with a Refunded Emissions Payments Scheme one such example. Under this mechanism, collected emissions payments are refunded to generators in proportion to energy output, with the net effect of the scheme resulting in each generator being subject to a net liability per MWh that is proportional to the difference between it’s own emissions intensity and the average emissions intensity of energy produced by regulated plant. A limitation of the scheme is the average emissions intensity of energy produced by regulated plant is only known after energy has been delivered, therefore there is some uncertainty with respect to each generator’s short-run marginal cost at the time of production. The presented framework addresses this issue by developing adaptive recalibration strategies that seek to make these net liabilities explicit. A rule-based updating strategy is developed, along with a highly generalisable model predictive control framework for updating scheme parameters. A case study using an agent-based framework is used to evaluate these mechanisms in the context of Australia’s National Electricity Market, and demonstrates how a policymaker could use such an approach to transparently update scheme parameters, and also accomplish auxiliary economic objectives.

A retrospective study of the 2012–2016 California drought and its impacts on the power sector

Over the period 2012–2016, the state of California in the United States (U.S.) experienced a drought considered to be one of the worst in state history. Drought’s direct impacts on California’s electric power sector are understood. Extremely low streamflow manifests as reduced hydropower availability, and if drought is also marked by elevated temperatures, these can increase building electricity demands for cooling. Collectively, these impacts force system operators to increase reliance on natural gas power plants, increasing market prices and emissions. However, previous investigations have relied mostly on ex post analysis of observational data to develop estimates of increases in costs and carbon dioxide (CO2) emissions due to the 2012–2016 drought. This has made it difficult to control for confounding variables (e.g. growing renewable energy capacity, volatile natural gas prices) in assessing the drought’s impacts. In this study, we use a power system simulation model to isolate the direct impacts of several hydrometeorological phenomena observed during the 2012–2016 drought on system wide CO2 emissions and wholesale electricity prices in the California market. We find that the impacts of drought conditions on wholesale electricity prices were modest (annual prices increased by $0–3 MWh−1, although much larger within-year increases are also observed). Instead, it was an increase in natural gas prices, punctuated by the 2014 polar vortex event that affected much of the Eastern U.S., which caused wholesale electricity prices to increase during the drought. Costs from the drought were very different for the state’s three investor owned utilities. Overall, we find that increased cooling demands (electricity demand) during the drought may have represented a larger economic cost ($3.8 billion) than lost hydropower generation ($1.9 billion). We also find the potential for renewable energy to mitigate drought-cased increases in CO2 emissions to be negligible, standing in contrast to some previous studies.

The impact of the integration of renewable energy sources in the electricity price formation: is the Merit-Order Effect occurring in Portugal?

This study investigates the relationship between wholesale electricity price behaviour and the integration of new renewable energy sources in the electricity system in Portugal. The research analyses two different samples, namely: the significant deployment of wind power versus the abrupt increase in the installed capacity of solar photovoltaic. Daily data from 2011 until 2019 and, a SARMAX/EGARCH approach has been conducted to assess the merit-order effect. The main results suggest that electricity production from wind power is decreasing the price of electricity but increasing its volatility. In addition, there is evidence for the leverage effect in electricity price.

A data‐driven approach to study the role of interconnectors in a future low‐carbon electricity supply system

This paper investigates the potential role of the electricity interconnectors in improving the security of supply in Great Britain (GB) in 2030. Real electricity demand and price data for GB and France in 2016 were used to understand the relationship between power exchange between the two countries and their wholesale electricity prices. A linear programming optimisation model was developed to find the economic power dispatch. Two interconnection links were considered; two-way trade interconnector with a capacity of 5.4 GW and a 12.3 GW import-only interconnector between GB and other states. The GB–France link transmits electricity from cheaper system to the more expensive one. The total electricity demand in 2030 will be 406 TWh. Gas-fired power plants w/wo CCS will provide 83 TWh of the total electricity demand, whereas nuclear power plants will produce 74 TWh. In addition, wind farms and solar PVs are expected to deliver ~120 TWh electricity. CHP units will provide 88 TWh electricity in 2030. The electricity traded between GB and France in 2030 was found to be 33 TWh, which is 160% larger compared with 2016. The power import from France is about 27 TWh and occurs in 59% of the time. For 64% of the time, the interconnector with France is fully loaded. The electricity imported via the 12.3 GW interconnector in 2030 is 1 TWh and mainly occurs during winter-time when the demand in GB is high. De-rated capacity margin was calculated based on instantaneous electricity demand and varies between −2% and 139%. The impact of the price of the imported electricity via the 12.3 GW link was investigated. Increasing the price of the imported electricity via the 12.3 GW link results in a higher capacity factor for all the generation options except the 12.3 GW interconnector link.

Modified deep learning and reinforcement learning for an incentive-based demand response model

Incentive-based demand response (DR) program can induce end users (EUs) to reduce electricity demand during peak period through rewards. In this study, an incentive-based DR program with modified deep learning and reinforcement learning is proposed. A modified deep learning model based on recurrent neural network (MDL-RNN) was first proposed to identify the future uncertainties of environment by forecasting day-ahead wholesale electricity price, photovoltaic (PV) power output, and power load. Then, reinforcement learning (RL) was utilized to explore the optimal incentive rates at each hour which can maximize the profits of both energy service providers (ESPs) and EUs. The results showed that the proposed modified deep learning model can achieve more accurate forecasting results compared with some other methods. It can support the development of incentive-based DR programs under uncertain environment. Meanwhile, the optimized incentive rate can increase the total profits of ESPs and EUs while reducing the peak electricity demand. A short-term DR program was developed for peak electricity demand period, and the experimental results show that peak electricity demand can be reduced by 17%. This contributes to mitigating the supply-demand imbalance and enhancing power system security.