Computationally efficient composite transmission expansion planning: A Pareto optimal approach for techno-economic solution

Gupta, Neeraj; Shekhar, Rajiv; Kalra, Prem Kumar

doi:10.1016/j.ijepes.2014.05.070

Cited by 15 publications

(4 citation statements)

References 22 publications

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“…There are many optimization techniques proposed to solve long-term generation expansion planning problems. They include linear programming (LP) [5], integer programming (IP) [6][7][8], non-linear programming (NLP) [9][10][11], dynamic programming (DP) [3,12,13], and metaheuristic method [14][15][16]. Although the complete optimization technique for long-term generation expansion planning is large-scale, highly constrained mixed-integer non-linear programming (MINLP) [7] with multiple decision criteria, uncertainties, and a dynamic nature [17], simplified models like mixed-integer linear programming (MILP) are widely used to avoid the considerable computational complexity associated with such non-linear methods or search algorithms [7].…”

Section: Literature Reviewmentioning

confidence: 99%

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Generation Expansion Planning with Energy Storage Systems Considering Renewable Energy Generation Profiles and Full-Year Hourly Power Balance Constraints

Diewvilai

Audomvongseree

2021

Energies

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This paper proposes a methodology to develop generation expansion plans considering energy storage systems (ESSs), individual generation unit characteristics, and full-year hourly power balance constraints. Generation expansion planning (GEP) is a complex optimization problem. To get a realistic plan with the lowest cost, acceptable system reliability, and satisfactory CO2 emissions for the coming decades, a complex multi-period mixed integer linear programming (MILP) model needs to be formulated and solved with individual unit characteristics along with hourly power balance constraints. This problem requires huge computational effort since there are thousands of possible scenarios with millions of variables in a single calculation. However, in this paper, instead of finding the globally optimal solutions of such MILPs directly, a simplification process is proposed, breaking it down into multiple LP subproblems, which are easier to solve. In each subproblem, constraints relating to renewable energy generation profiles, charge-discharge patterns of ESSs, and system reliability can be included. The proposed process is tested against Thailand’s power development plan. The obtained solution is almost identical to that of the actual plan, but with less computational effort. The impacts of uncertainties as well as ESSs on GEP, e.g., system reliability, electricity cost, and CO2 emission, are also discussed.

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Section: Literature Reviewmentioning

confidence: 99%

“…To represent the actual characteristic of ESSs, several constraints needed to be considered. These constraints are ESS energy transition, power rating boundary, and state of charge boundary as shown in ( 11)- (16).…”

Section: Energy Storage System Operating Constraintmentioning

confidence: 99%

Generation Expansion Planning with Energy Storage Systems Considering Renewable Energy Generation Profiles and Full-Year Hourly Power Balance Constraints

Diewvilai

Audomvongseree

2021

Energies

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“…Research works [60], [61] studied distributed generation planning with game theory and probabilistic modelling, respectively. Some works considered an integrated model for both generation and transmission capacity [13], [62], while [15] studied how generation capacity decisions impact network planning.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Strategic Renewable Energy, Grid and Storage Capacity Investments via Stackelberg-Cournot Modelling

et al. 2021

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With increasing decarbonisation and accessibility to our energy systems and markets, there is a need to understand and optimise the value proposition for different stakeholders. Game-theoretic models represent a promising approach to study strategic interactions between self-interested private energy system investors. In this work, we design and evaluate a game-theoretic framework to study strategic interactions between profit-maximising players that invest in network, renewable generation and storage capacity. Specifically, we study the case where grid capacity is developed by a private renewable investor, but line access is shared with competing renewable and storage investors, thus enabling them to export energy and access electricity demand. We model the problem of deducing how much capacity each player should build as a non-cooperative Stackelberg-Cournot game between a dominant player (leader) who builds the power line and renewable generation capacity, and local renewable and storage investors (multiple followers), who react to the installation of the line by increasing their own capacity. Using data-driven analysis and simulations, we developed an empirical search method for estimating the game equilibrium, where the payoffs capture the realistic operation and control of the energy system under study. A practical demonstration of the underlying methodology is shown for a real-world grid reinforcement project in the UK. The methodology provides a realistic mechanism to analyse investor decision-making and investigate feasible tariffs that encourage distributed renewable investment, with sharing of grid access.

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“…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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Computationally efficient composite transmission expansion planning: A Pareto optimal approach for techno-economic solution

Cited by 15 publications

References 22 publications

Generation Expansion Planning with Energy Storage Systems Considering Renewable Energy Generation Profiles and Full-Year Hourly Power Balance Constraints

Generation Expansion Planning with Energy Storage Systems Considering Renewable Energy Generation Profiles and Full-Year Hourly Power Balance Constraints

Analysis of Strategic Renewable Energy, Grid and Storage Capacity Investments via Stackelberg-Cournot Modelling

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

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