Electricity Price Volatility Research Articles

FarmConners market showcase results: wind farm flow control considering electricity prices

Abstract. The EU and UK have made ambitious commitments under the net-zero plans to decarbonise their economies by 2050. For this, offshore wind will play a major role, significantly contributing to a paradigm shift in the power generation and greater volatility of electricity prices. The operating strategy of wind farms should therefore move from power maximisation to profit maximisation which includes income from providing power system services and the reduction of maintenance costs. Wind farm flow control (WFFC) is a key enabler for this shift through mitigation of wake effects in the design and operation phases. The results of the FarmConners market showcases presented here are the first attempt to economically assess WFFC strategies with respect to electricity market prices. Here, we present a conceptual simulation study starting from individual turbine control and extend it to layouts with 10 and 32 turbines operated with WFFC based on the results of five participants. Each participant belonged to a different research group with their respective simulation environments, flow models and WFFC strategies. Via a comparative analysis of relative WFFC benefits estimated per participant, the implications of wind farm size, the applied control strategy and the overall model fidelity are discussed in zero-subsidy scenarios. For all the participants, it is seen that the income gain can differ significantly from the power gain depending on the electricity price under the same inflow, and a favourable control strategy for dominant wind directions can pay off even for low electricity prices. However, a strong correlation between income and power gain is also observed for the analysed high-electricity-price scenarios, underlining the need for additional modelling capabilities to carry out a more comprehensive value optimisation including lower prices and system requirements driven cases.

The role of electricity flows and renewable electricity production in the behaviour of electricity prices in Spain

Electricity markets are becoming increasingly interconnected, in part, to deal with unpredictability and imbalances in electricity prices. This paper assesses how electricity flows and generation from wind and solar photovoltaic (PV) power impact the volatility and mean of day-ahead electricity prices in Spain. It finds there is a merit order effect from wind and solar PV power at most times of the day, which varies in magnitude for each of the 24 h. Electricity production from wind power is also found to play a clear role in increasing price volatility at all hours. Electricity inflow is shown to decrease both the mean and volatility of the electricity prices. The volatility of electricity prices is revealed to be highly persistent, and thus prone to large transmission shocks. This volatility is also susceptible to new shocks, which may indicate a tendency by market participants to overreact to unexpected shocks in electricity prices, possibly due to the relatively small size of the Iberian electricity market.

Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics

Incorporating phase-change material (PCM) wallboards into building envelopes can help reduce electricity costs associated with heating and enhance demand flexibility in dynamic building energy management in response to electricity price volatility. The conventional control strategies adopted in current building energy management systems are ‘reactive’ in nature, hence, they are not ready to exploit the full potential of PCM wallboards for price-based, demand-responsive building control applications. This study proposes a model predictive control (MPC) framework for price-based, demand-responsive control of PCM-wallboard-enhanced space heating systems to minimize electricity costs and maximize demand flexibility by fully exploiting PCM wallboards. A high-fidelity, linear PCM wallboard and building thermodynamics model are developed for computationally efficient building control. The model is then incorporated into an MPC framework considering the PCM wallboard dynamics and dynamic electricity prices. A simulation case study is conducted for a single-family house employing the proposed MPC approach to control its space heating system. Compared to conventional control, the MPC approach reduces space heating electricity costs by more than 60 %. The MPC approach also shifts more than 80 % of peak heating loads to off-peak periods, much higher than conventional control achieves (less than40 %). The simulation results also show good synergistic benefits of MPC in fully exploiting PCM wallboards in optimizing building space heating operation: achieving additional 31.1 % electricity cost savings and 38.7 % peak load shifting than without leveraging PCM wallboards. In contrast, conventional control cannot achieve such synergistic benefits. The proposed MPC framework is also proven computationally viable for real-time building control.

The impact of wind and solar power generation on the level and volatility of wholesale electricity prices in Greece

We investigate the impact of wind and solar power generation on the level and volatility of wholesale electricity prices in the Greek electricity market from August 2012 to December 2018. In the context of a GARCH-in-Mean model the empirical findings suggest the existence of the merit-order effect which is stronger in the case of wind power. Controlling for regulatory mechanisms that may affect price volatility, we find that while overall renewables have decreased price volatility, wind power tends to increase it and solar power tends to decrease it. Furthermore, during peak hours, wind and solar power generation tend to decrease price volatility, supporting the hypothesis that renewables’ output reduces the volatility of wholesale electricity prices when it is positively correlated with the electricity load. Finally, we find that the increase in the price-cap of the Greek wholesale electricity market was associated with a reduction in the volatility of wholesale electricity prices. This finding highlights the importance of the market structure and the degree of vertical integration of participants in liberalized electricity markets, which determines their behavior while also affecting market price volatility.

The economic cost of hydropower environmental constraints under decreasing price volatility

AbstractHydropower operation optimization in river systems involves market prices, technological constraints affecting the efficiency of turbines, and flow constraints set by an environmental regulator. Comprehensive environmental flow regulation includes the ecological state of a river system and the impact on hydropower value. This article studies the impacts of environmental constraints on hydropower value under varying electricity price volatility scenarios. The effects of maximum flow, minimum flow, and flow ramping constraints are studied analytically and quantitatively. We frame the lost hydropower value as the economic cost of these constraints. We show that the economic costs of the environmental constraints decrease with lower price volatility. We use a marginal cost and marginal benefit framework to illustrate that the optimal flow constraint should be tightened if the price volatility decreases in the electricity market. Our approach illustrates how electricity price volatility influences the analysis of optimal environmental constraints in regulated river systems. Finally, we analyse the effect of different environmental flow release options in fishways on the economic cost of a fishway. If the hydropower operator can optimize the fishway flow allocation, then the loss in hydropower value is lower than under a constant fishway flow.

Multi-time scale trading profit model of pumped storage power plant for electricity market

Pumped storage power plant (PSPP) has the upper hand on economy and cleanness. It also has the functions of frequency regulation, phase regulation, and spare, which have been instrumental in maintaining the stability of power system operation. But now the mechanism for PSPP to become involved in electricity market transactions in China is imperfect. How to properly establish a multi-time scale trading profit model and reasonably allocate the capacity of PSPP has been instrumental in realizing the economic operation of the power system. So, this article analyzes the mechanism for PSPP to become involved in electricity market trading by providing combined electricity supply services and ancillary services, and establishes an optimization model with respect to economic optimization. At the same time, considering the volatility of electricity prices in the spot market, the risk of PSPP becoming involved in electricity market trading is measured by conditional Value at Risk (CVaR) to achieve economic optimization while minimizing the risk. The case studies demonstrate that the proposed profit model can enhance the revenue and decrease the risk of PPSP.

The synergy between the photovoltaic power systems and battery-powered electric ferries in the isolated energy system of an island

Islands have increased potential to integrate renewable energy sources (RES) into their energy systems and shift towards sustainable decarbonization. The intermittent electricity causes issues such as the mismatch between the energy demand and supply, the grid instability and the electricity price volatility. The integration of the battery-powered electric ferries (BEF) into the isolated energy system (IES) powered with RES can be considered as an economically and environmentally friendly solution for matching the demand with the supply, since all-electric ships can connect to the grid and provide positive or negative balancing power. The carbon dioxide (CO2) emissions from shipping operations can be reduced to zero by utilizing batteries as a power source. This paper deals with a holistic approach that demonstrates opportunities that implementation of BEF offers in the framework of IES of an island. A method for modelling energy supply for a 100% electric ferry transport is developed to reduce the critical excess electricity production (CEEP) from the photovoltaic (PV) power systems. The energy modelling of the IES was carried out in EnergyPLAN program. The results confirmed that synergy between the PV power systems and BEF can significantly reduce the CEEP, the operating system costs and the emissions of CO2.

Investment strategies under stochastic electricity prices and implications for charging infrastructure network coverage: A case of photovoltaic pavements

For enhancing the charging infrastructure network coverage in populated areas, photovoltaic pavement (PVP) has been considered an innovative option that caters to distributed power generation and electric vehicles (EVs) charging in motion. However, the promotion of PVP has not yet served its intended purpose. This paper develops a gross net present value (GNPV) model to investigate the optimal timing to invest in PVP under stochastic electricity prices and time-dependent investment costs. Based on the photovoltaic highway project in Shandong, China, our numerical results reveal that the volatility of electricity prices, construction investment costs, and solar panels’ energy conversion efficiency significantly restricts the diffusion of PVP, resulting in the current dilemma of PVP adoption. However, the rapid decline in investment costs and the rapid increase in the number of EVs are not always effective in accelerating the PVP investment process, especially in some high electricity price regions, such as the southern areas of China. The findings of this paper will provide theoretical support and decision-making recommendations for the management evaluation of PVP projects, the optimization of charging facility networks, and the formulation of EV incentive measures.

Distributed ESS Capacity Decision for Home Appliances and Economic Analysis

The imbalance between the supply and demand of electricity is becoming expansive due to the increase in renewable energy penetration. Consequently, the time-varying electricity price is proposed as a tool to manage the mismatch. To cope with the electricity price difference across time and to extract the benefit to the limit, an energy storage system (ESS) can be a good tool to smooth out electricity usage. Nevertheless, ESS can be expensive due to the high installation cost and centrally managed system. In this paper, we propose a distributed ESS installed in each home appliance. Separately installed batteries can avoid sizeable initial investment costs. We calculated the benefit of the system that mainly stemmed from the electricity price difference and compared the benefits across various sizes of the battery system. In addition, the charging/discharging schedule depends on the power consumption profile and electricity price distribution. Decreasing battery prices and rising electricity price volatility can increase the system’s potential benefit.

A robust statistical analysis of the role of hydropower on the system electricity price and price volatility

Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage and large reservoir storage) for providing energy in times of high demand on the grid (S. Rehman, L M Al-Hadhrami, and M M Alam), (2015 Renewable and Sustainable Energy Reviews, 44, 586–98). This study examines the impact of hydropower on system electricity price and price volatility in the region served by the New England Independent System Operator (ISONE) from 2014-2020 (ISONE, ISO New England Web Services API v1.1.” https://webservices.iso-ne.com/docs/v1.1/, 2021. Accessed: 2021-01-10). We perform a robust holistic analysis of the mean and quantile effects, as well as the marginal contributing effects of hydropower in the presence of solar and wind resources. First, the price data is adjusted for deterministic temporal trends, correcting for seasonal, weekend, and diurnal effects that may obscure actual representative trends in the data. Using multiple linear regression and quantile regression, we observe that hydropower contributes to a reduction in the system electricity price and price volatility. While hydropower has a weak impact on decreasing price and volatility at the mean, it has greater impact at extreme quantiles (>70th percentile). At these higher percentiles, we find that hydropower provides a stabilizing effect on price volatility in the presence of volatile resources such as wind. We conclude with a discussion of the observed relationship between hydropower and system electricity price and volatility.

Short Term Load Forecasting of Residential and Commercial Consumers of Karnataka Electricity Board using CFNN

Electricity use and its access are correlated in the economic development of any country. Economically, electricity cannot be stored, and for stability of an electrical network a balance between generation and consumption is necessary. Electricity demand depends on various factors like temperature, everyday activities, time of day, days of the week days/Holidays. These parameters have led to price volatility and huge spikes in electricity prices. The research work proposes a short term Load prediction Model for LT2 (residential consumers), LT3 (Commercial Consumers) of Karnataka State Electricity Board using Cascaded Feed Forward Neural Network (CFNN). MATLAB software is utilized to design and test the forecasting model for predicting the power consumption. Furthermore, a shallow feed forward neural network-based prediction model is constructed and evaluated for performance comparison. The Performance metrics include Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The suggested STLF CFNN prediction model outperformed shallow feed forward networks on both performance metrics with prediction errors of less than 1%.

Real-time optimal control for end-users with energy storage and renewable sources

In this article, we investigate the problem of real-time optimal control for end-users with integrated renewable energy generation and energy storage. The proposed a novel and comprehensive model consists of the electric vehicle (EV), renewable energy, the residential combined heat and power (resCHP) system and energy storage systems (e.g., battery and water tank). By jointly considering the operational constraints of energy storage, the stochastic behavior of renewable energy production and demand, and the volatility of electricity prices, the energy management problem is transformed into a stochastic optimization problem that minimizes the end-user’s the long term time-averaged cost. First, to eliminate the effect of storage control coupling over time, the capacity constraints (physical constraints) of the stochastic optimization problem are relaxed to time-averaged constraints. Secondly, we solve this problem by designing an real-time online algorithm based on the improved Lyapunov optimization. The proposed algorithm has a significant feature that does not require any statistical information about the stochastic system inputs (e.g., the uncertain power and heat demand, the renewable energy production, and the time-varying electricity prices, and so on). Furthermore, the performance analysis of the proposed algorithm shows that it not only ensures that all control decisions are executed within the limited storage capacity, but also provides a near-optimal solution for the optimization problem. Finally, simulation results demonstrate that the proposed algorithm can reduce 74.4% of the total cost compared with the benchmark approach.

Simultaneous mixed‐integer dynamic scheduling of processes and their energy systems

AbstractIncreasingly volatile electricity prices make simultaneous scheduling optimization desirable for production processes and their energy systems. Simultaneous scheduling needs to account for both process dynamics and binary on/off‐decisions in the energy system leading to challenging mixed‐integer dynamic optimization problems. We propose an efficient scheduling formulation consisting of three parts: a linear scale‐bridging model for the closed‐loop process output dynamics, a data‐driven model for the process energy demand, and a mixed‐integer linear model for the energy system. Process dynamics is discretized by collocation yielding a mixed‐integer linear programming (MILP) formulation. We apply the scheduling method to three case studies: a multiproduct reactor, a single‐product reactor, and a single‐product distillation column, demonstrating the applicability to multiple input multiple output processes. For the first two case studies, we can compare our approach to nonlinear optimization and capture 82% and 95% of the improvement. The MILP formulation achieves optimization runtimes sufficiently fast for real‐time scheduling.

Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization

Accurate electricity price forecasts is the common concern of market participants. With the integration of high penetration of wind and solar energy resources into the power system, the renewable energy sources will have a great impact on the electricity price volatility undoubtedly. In this regard, a novel attention mechanism (AM) based electricity price forecasting model for electricity market with high proportion of renewable energy is proposed in this paper. In order to investigate the effect of renewable energy on the electricity price prediction, the wind power generation, solar power generation, predicted load and the historical price series are simultaneously taken as the input features. In the data preprocessing stage, the empirical wavelet transform (EWT) is applied to decompose each of the input features into multiple components to avoid learning the autocorrelation of the original sequence. In the model training stage, a hybrid AM-based long short-term memory network (LSTM) is proposed as the forecasting model, aiming to make full use of the AM to dynamically evaluate the importance of different input feature. Furthermore, the crisscross optimization algorithm (CSO) is adopted to retrain the parameters of fully-connected layer so as to further enhance the generalization ability. The proposed method is validated on the datasets of Danish electricity market with a high proportion of renewable energy, and the experimental results show that the proposed model is superior to other hybrid models involved in this study.

Climate Change and the Vulnerability of Germany’s Power Sector to Heat and Drought

The effects of extreme weather events and the resilience of the energy sector have become the subject of regulatory initiatives and ongoing research. We demonstrate the vulnerability of the German power sector to climate change and provide a qualitative and quantitative analysis of emerging risks from two types of extreme weather events: droughts and high temperatures. Our analysis is based on datasets covering temperature and drought data for the last 40 years. We present evidence of a higher frequency of power plant outages as a consequence of droughts and high temperatures. To characterize the vulnerability of the power sector we develop a capacity-adjusted drought index. The results are used to assess the monetary loss of power plant outages due to heatwaves and droughts and losses to consumers due to higher wholesale electricity prices and price volatility. An increasing frequency of such extreme weather events will aggravate the observed problem.

Hedging Wind Power Risk Exposure through Weather Derivatives

We introduce the industrial portfolio of a wind farm of a hypothetical company and its valuation consistent with the financial market. Next, we propose a static risk management policy originating from hedging against volumetric risk due to drops in wind intensity and we discuss the consequences. The hedging effectiveness firstly requires adequate modeling calibration and an extensive knowledge of these atypical financial (commodity) markets. In this hedging experiment, we find significant benefits for weather-sensitive companies, which can lead to new business opportunities. We provide a new financial econometrics approach to derive weather risk exposure in a typical wind farm. Our results show how accurate risk management can have a real benefit on corporate revenues. Specifically, we apply the spot market price simulation (SMaPS) model for the spot price of electricity. The parameters are calibrated using the prices of the French day-ahead market, and the historical series of the total hourly load is used as the final consumption. Next, we analyze wind speed and its relationship with electricity spot prices. As our main contribution, we demonstrate the effects of a hypothetical hedging strategy with collar options implemented against volumetric risk to satisfy demand at a specific time. Regarding the hedged portfolio, we observe that the “worst value” increases considerably while the earnings-at-risk (EaR) decreases. We consider only volumetric risk management, thus neglecting the market risk associated with electricity price volatility, allowing us to conclude that the hedging operation of our industrial portfolio provides substantial benefits in terms of the worst-case scenario.

Integrated Stochastic Optimal Self-Scheduling for Two-Settlement Electricity Markets

The complexity of current electricity wholesale markets and the increased volatility of electricity prices because of the intermittent nature of renewable generation make independent power producers (IPPs) face significant challenges to submit offers. This challenge increases for those owning traditional coal-fired thermal generators and renewable generation. In this paper, an integrated stochastic optimal strategy is proposed for an IPP using the self-scheduling approach through its participation in both day-ahead and real-time markets (i.e., two-settlement electricity markets) as a price taker. In the proposed approach, the IPP submits an offer for all periods to the day-ahead market for which a multistage stochastic programming setting is explored for providing real-time market offers for each period as a recourse. This strategy has the advantage of achieving overall maximum profits for both markets in the given operational time horizon. Such a strategy is theoretically proved to be more profitable than alternative self-scheduling strategies as it takes advantage of the continuously realized scenario information of the renewable energy output and real-time prices over time. To improve computational efficiency, we explore polyhedral structures to derive strong valid inequalities, including convex hull descriptions for certain special cases, thus strengthening the formulation of our proposed model. Polynomial-time separation algorithms are then established for the derived exponential-sized inequalities to speed up the branch-and-cut process. Finally, both numerical and real case studies demonstrate the potential of the proposed strategy. Summary of Contribution: This paper develops innovative models and methods to study a family of practically important problems via the interactions of operations research and computing. Specifically, this paper provides in-depth analyses of innovative stochastic optimization modeling approaches and develops computationally efficient polyhedral results. The paper also verifies the effectiveness of proposed analyses via extensive computing experiments.

Price spikes, temporary price caps, and welfare effects of regulatory interventions on wholesale electricity markets

There is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. While price spikes are needed to cover the fixed costs of power plants, they may also signal market flaws and anti-competitive behavior. Regulatory authorities have imposed market price caps to protect consumers and prevent abusive supplier behavior. In addition, some regulators have imposed temporary price caps during or after severe events; however, these caps may be driven by political motives rather than economic logic in weak institutional settings. This paper evaluates the welfare effect of the temporary price cap implemented in 2017 on the Turkish electricity market. By using matching and panel data methods, we show that the temporary price cap reduced the total welfare but did not affect market clearing price and projected supply. Our detailed analysis shows that this decision was driven by non-economic motives, and identifies a number of fundamental problems in the Turkish market that limit the effective functioning of the market.

The impact of optimally dispatched energy storage devices on electricity price volatility

A high level of electricity price volatility increases financial risks and safety issues in power system operations. Storage devices as an efficient solution to mitigating price volatility have attracted extensive attention. In this paper, the mechanism of how storage devices impact price volatility is investigated at first. Through analyzing the connection between an economic dispatch problem and its Lagrange dual, we reveal that the capacity and charge/discharge power of a storage device installed at a node have an aggregate impact on the range of the price changes of that node. A model to calculate the sensitivity of price volatility to storage device parameters is proposed. To determine the optimal nodes to place storage devices and the optimal storage capacities to constrain the price change levels, this paper develops a chance-constrained optimization model, and a scenario-based approach is adopted to solve the problem. The IEEE 24-bus system is employed in the case studies, associated with Gurobi and Julia. Simulation studies show that if there is one node with the highest price change all the time, the price volatility must have the highest sensitivity to the storage device on that node. However, in the case where there are multiple nodes having the highest price changes at different times, a node with high price change does not necessarily mean high sensitivity. The simulation studies verify that the chance-constrained optimization model is effective to suppress excess price changes.

Value of power-to-gas as a flexibility option in integrated electricity and hydrogen markets

This paper analyzes the economic potential of Power-to-Gas (PtG) as a source of flexibility in electricity markets with both high shares of renewables and high external demand for hydrogen. The contribution of this paper is that it develops and applies a short-term (hourly) partial equilibrium model of integrated electricity and hydrogen markets, including markets for green certificates, while using a welfare-economic framework to assess the market outcomes. We find that strongly increasing the share of renewable electricity makes electricity prices much more volatile, while the presence of PtG reduces this price volatility. However, a large demand for hydrogen from outside the electricity sector reduces the impact of PtG on the volatility of electricity prices. In a scenario with a high external hydrogen demand, PtG can deliver positive benefits for some groups as it can provide hydrogen at lower costs than Steam Methane Reforming (SMR) during hours when electricity prices are low, but these positive welfare effects are outweighed by the fixed costs of PtG assets plus the costs of replacing a less expensive energy carrier (natural gas) with a more expensive one (hydrogen). Investments in PtG are profitable from a social-welfare perspective when the induced reduction in carbon emissions is valued at 150–750 euro/ton. Hence, at lower carbon prices, PtG can only become a valuable provider of flexibility when installation costs are significantly reduced and conversion efficiencies of electrolysers increased.