Risk-minimisation in electricity markets: Fixed price, unknown consumption

Tegnér, Martin; Ernstsen, Rune Ramsdal; Skajaa, Anders; Poulsen, Rolf

doi:10.1016/j.eneco.2017.10.014

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…V p * is obtained in a similar manner with a slight modification: first by taking the mean of hourly load for peak hours and then subtracting V b * from the result. Assuming spot-price and consumption load is uncorrelated, hedging at the expected load results in the minimum variance position; this is a robust approach used by industry (Tegnér et al (2017)). While it is entirely plausible that non-public, proprietary models exist that perform better than this benchmark, we do not concern ourselves with what those might be.…”

Section: Hedging Resultsmentioning

confidence: 99%

“…Close to our work is Tegnér et al (2017) who assume the retailer's perspective for risk management, which is a similar position adopted by Oum, Oren, and Deng (2006). The former jointly model the electricity spot price and consumption load with a two-dimensional Ornstein-Uhlenbeck process and a seasonality component.…”

Section: Related Workmentioning

confidence: 99%

“…The setup of the following hedging problem where the aim is to accurately replicate an uncertain future financial obligation is similar to Tegnér et al (2017). This is usually achieved by using financial derivatives such as futures and forwards.…”

Section: Hedging Problemmentioning

confidence: 99%

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A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes

Poh,

Roberts,

Tegnér

2019

Preprint

Self Cite

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The non-storability of electricity makes it unique among commodity assets, and it is an important driver of its price behaviour in secondary financial markets. The instantaneous and continuous matching of power supply with demand is a key factor explaining its volatility. During periods of high demand, costlier generation capabilities are utilised since electricity cannot be stored and this has the impact of driving prices up very quickly. Furthermore, the non-storability also complicates physical hedging. Owing to these, the problem of joint pricequantity risk in electricity markets is a commonly studied theme.We propose using Gaussian Processes (GPs) to tackle this problem since GPs provide a versatile and elegant non-parametric approach for regression and timeseries modelling. However, GPs scale poorly with the amount of training data due to a cubic complexity. These considerations suggest that knowledge transfer between price and load is vital for effective hedging, and that a computationally efficient method is required. To this end, we use the coregionalized (or multitask) sparse GPs which addresses the aforementioned issues.To gauge the performance of our model, we use an average-load strategy as comparator. The latter is a robust approach commonly used by industry. If the spot and load are uncorrelated and Gaussian, then hedging with the expected load will result in the minimum variance position.Our main contributions are twofold. Firstly, in developing a coregionalized 1

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Section: Hedging Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes

Poh,

Roberts,

Tegnér

2019

Preprint

Self Cite

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“…Barbecho Bautista et al proposed a charging management scheme for electric vehicles, which considers the charging time of each candidate charging station and the total travel time to the destination [7] . In addition, many scholars have studied the charging scheduling strategy of electric vehicles from the perspective of electricity price adjustment, including fixed electricity price [8] , time-of-use price (TOU) [9] and real-time price (RTP) [10] . Ming et al analyzes the electric vehicle route selection and charging navigation model under the time-ofuse pricing mechanism, which take the user travel time and charging cost as the optimization goals, respectively [11] .…”

Section: Introductionmentioning

confidence: 99%

Electric vehicle charging path planning considering the state of EV pile-road-grid

Yang

Wang

et al. 2022

Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022)

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Aiming at the problem of “1 hour to charge and 4 hours just to queue” during the National Day holiday in 2021, in order to reduce the problems of poor charging experience and reduced confidence in high-speed travel caused by too long charging queuing time, this paper proposes a charging path planning. Based on the state model of electric vehicles (EV), charging stations, traffic network and distribution network, this paper fully considers the charging resources around the expressway network when planning the charging path for users on the expressway network, and proposes a new method considering the “EV-pile-road-grid” state electric vehicle charging path planning. By calling the Baidu map API interface and the data of the charging piles of the Internet of Vehicles Platform, the optimized selection of charging stations is completed, and a feasible navigation path with the shortest travel (time) is finally formed.

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“…e pricing mechanism of EV charging stations should also be investigated. e commonly studied pricing strategies mainly include fixed electricity price [18], time-of-use (TOU) price [19], and real-time price (RTP) [20]. RTP is a method of dynamically adjusting electricity price via the relationship between the power system supply and demand [21].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Large‐Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

Hou

Luo

et al. 2021

Complexity

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With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy.

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Risk-minimisation in electricity markets: Fixed price, unknown consumption

Cited by 11 publications

References 13 publications

A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes

A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes

Electric vehicle charging path planning considering the state of EV pile-road-grid

Multiobjective Optimization of Large‐Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

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