Sustainable resource planning in energy markets

Kamalinia, Saeed; Shahidehpour, Mohammad; Wu, Lei

doi:10.1016/j.apenergy.2014.07.065

Cited by 39 publications

(15 citation statements)

References 32 publications

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“…Moreover, in all of them, transmission constraints are ignored. In Kamalinia et al (2014), which uses mean-variance optimization to study equilibria in a competitive market, a sensitivity analysis with respect to the transmission constraints shows that, as may be expected, these constraints have a major impact on generator's payoffs, and therefore on investment and the impact of risk aversion. The latter is not quantified, however.…”

Section: Risk-averse Generation Planningmentioning

confidence: 94%

Does risk aversion affect transmission and generation planning? A Western North America case study

et al. 2017

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We investigate the effects of risk aversion on optimal transmission and generation expansion planning in a competitive and complete market. To do so, we formulate a stochastic model that minimizes a weighted average of expected transmission and generation costs and their conditional value at risk (CVaR). We show that the solution of this optimization problem is equivalent to the solution of a perfectly competitive risk-averse Stackelberg equilibrium, in which a risk-averse transmission planner maximizes welfare after which risk-averse generators maximize profits. This model is then applied to a 240-bus representation of the Western Electricity Coordinating Council, in which we examine the impact of risk aversion on levels and spatial patterns of generation and transmission investment. Although the impact of risk aversion remains small at an aggregate level, state-level impacts on generation and transmission investment can be significant, which emphasizes the importance of explicit consideration of risk aversion in planning models.1 As discussed in , both the Midcontinent and the California Independent System Operators use engineering rules that aim at identifying "robust" or "least regret" transmission projects. Although risk aversion is not explicitly mentioned in these studies, their methodologies suggest that the planning authorities are more concerned with worst-case situations (i.e., risk averse preferences) than with the expected performance of the selected projects across all considered scenarios (i.e., risk neutrality).

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Section: Risk-averse Generation Planningmentioning

confidence: 94%

Does risk aversion affect transmission and generation planning? A Western North America case study

et al. 2017

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“…Akbari et al [29] provided a stochastic short term transmission planning model based in Monte Carlo simulations, while Zeng et al [30] considered a multi-level optimisation approach for active distribution system planning with renewable energy harvesting, taking into consideration reinforcement and operational constraints. In [31,32], the authors studied distributed generation expansion planning with game theory and probabilistic modelling with strategic interactions, respectively. Other works consider an integrated model for both generation and transmission capacity [33,34] or in [35] where the effect of generation capacity on transmission planning was examined.…”

Section: Review Of Game Theory and Artificial Intelligence Techniquesmentioning

confidence: 99%

Game-theoretic modeling of curtailment rules and network investments with distributed generation

et al. 2017

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Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of several curtailment rules widely used in UK renewable energy projects, and their effect on the viability of renewable generation investment. Moreover, we propose a new curtailment rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. In this work, we provide a solution to this problem by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private transmission line investor and local renewable generators, who are required to pay a transmission fee to access the line. We provide a method for computing the equilibrium of this game, using a model that captures the stochastic nature of renewable energy generation and demand. Finally, we use the practical setting of a grid reinforcement project from the UK and a large dataset of wind speed measurements and demand to validate our model. We show that charging a transmission fee as a proportion of the feed-in tariff price between 15%-75% would allow both investors to implement their projects and achieve desirable distribution of the profit. Overall, our results show how using gametheoretic tools can help network operators to bridge the knowledge gap about setting the optimal curtailment rule and determining transmission charges for private network infrastructure. $ This paper builds on significant extensions, both in theoretical results and new datasets, of preliminary work presented at two international conferences: AAMAS 2016 [1] and IEEE ISGT Europe 2016 [2].it might have negative effects on the operation, resilience and safety of the electricity grid. RES are intermittent and have variable power outputs due to constantly changing primary resources and weather patterns, which are difficult to predict. The challenges faced by network operators relate to reverse power flows, increased power losses, harmonics, voltage fluctuations, thermal capacity of equipment, frequency and voltage regulation and can compromise the system reliability [5].An additional barrier is that grid infrastructure is inadequate to support continuous RES development or distributed generation (DG), especially in the area of distribution networks. Often high investment takes place in remote areas of the grid, where projects face favourable resource conditions and planning approval. Typically, in the UK, ...

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“…Evolutionary algorithms have been extensively used to solve the GEP optimization problem, ie, GA, [29][30][31][37][38][39][40] particle swarm optimization, 24,32,40-43 differential evolution, 39,40,44,45 ant colony optimization, 39,46 and evolution strategies (ES). 39,40 Moreover, some hybrid approaches combining GA with simulated annealing, 47 GA with generalized Benders decomposition, 48 and GA with dynamic programming 49 have been proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These are related to commodity prices, electricity demand, and the evolution of renewable energy technologies. The unpredictability of commodity values is also considered by Kamalinia et al, where a resource planning model integrating transmission network expansion is examined. In their work, an incomplete information noncooperative game‐theoretic method is implemented in order each participating generation company to decide its strategic capacity expansion investments.…”

Section: Literature Reviewmentioning

confidence: 99%

Relaxation of quantitative energy objectives on generation expansion planning: A computational and policy study

Tsalavoutis

Vrionis

Tolis

2017

Int Trans Electr Energ Syst

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Summary In the present research, a decision support tool is presented for the optimization of generation expansion planning (GEP) of semiliberalized electricity markets under specified quantitative energy objectives. The evolution of the anticipated System Marginal Price is estimated overtime, in conjunction with power sector's structure. A hybrid solver has been used for that task. It comprises of the Improved Stochastic Ranking Evolution Strategy (ISRES) and the Interior Point Algorithm (IPA), in an attempt to exploit their distinct characteristics. The quantitative energy objectives are represented as constraints. A relaxation factor is applied on the aforementioned constraints and its effect on the power sector's formation and the optimization procedure is demonstrated through a series of representative computational experiments. The results derived from using various relaxation levels are compared with a base case where the energy constraints are not embedded. Capacity orders are extracted denoting the optimal steps towards accomplishing the energy objectives. The evolution of some core indicators of the energy system is compared amongst the investigated cases. Relaxation facilitated the optimization procedure without intervening in GEP scheduling by much. Moreover, ISRES‐IPA is benchmarked against commonly used solvers, demonstrating improved performance on the GEP model's optimization. The decision support tool presented and the conclusions extracted from this study may be of interest to energy policy makers, planners, and investors.

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Sustainable resource planning in energy markets

Cited by 39 publications

References 32 publications

Does risk aversion affect transmission and generation planning? A Western North America case study

Does risk aversion affect transmission and generation planning? A Western North America case study

Game-theoretic modeling of curtailment rules and network investments with distributed generation

Relaxation of quantitative energy objectives on generation expansion planning: A computational and policy study

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