Dynamic Robust Transmission Expansion Planning

Recent breakthroughs in Transmission Network Expansion Planning (TNEP) have demonstrated that the use of robust optimization, as opposed to stochastic programming methods, renders the expansion planning problem considering uncertainties computationally tractable for real systems. However, there is still a yet unresolved and challenging problem as regards the resolution of the dynamic TNEP problem (DTNEP), which considers the year-by-year representation of uncertainties and investment decisions in an integrated way. This problem has been considered to be a highly complex and computationally intractable problem, and most research related to this topic focuses on very small case studies or used heuristic methods and has lead most studies about TNEP in the technical literature to take a wide spectrum of simplifying assumptions. In this paper an adaptive robust transmission network expansion planning formulation is proposed for keeping the full dynamic complexity of the problem. The method overcomes the problem size limitations and computational intractability associated with dynamic TNEP for realistic cases. Numerical results from an illustrative example and the IEEE 118-bus system are presented and discussed, demonstrating the benefits of this dynamic TNEP approach with respect to classical methods.Comment: 10 pages, 2 figures. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TPWRS.2016.2629266, IEEE Transactions on Power Systems 201

Climate policy costs of spatially unbalanced growth in electricity demand: the case of datacentres. ESRI Working Paper No. 657 March 2020

We investigate the power system implications of the anticipated expansion in electricity demand by datacentres. We perform a joint optimisation of Generation and Transmission Expansion Planning considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system, even under the cheapest policy option. A renewable energy target is more costly than a technology-neutral carbon reduction policy, and the divergence in costs increases non-linearly in electricity demand. Moreover, a carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. High renewable targets crowd out other low-carbon options such as Carbon Capture and Sequestration. The results suggest that energy policy should be reviewed to focus on technology-neutral carbon reduction policies

TRANSMISSION NETWORK EXPANSION WITH TRANSMISSION LOADING RELIEF

Transmission planning should seek to maintain or improve system security over time and facilitate robust wholesale power markets by improving transmission capacity for bulk power transfers across wide regions It includes finding the optimal plan for the electrical system expansion, it must specify the transmission lines and/or transformers that should be constructed so that the system to operate in an adequate way and in a specified planning horizon. In this paper a methodology is proposed for choosing the best transmission expansion plan using Transmission security based on contingency analysis. A procedure using sensitivity analysis is used to evaluate potential transmission connections and that provide the most improvements to overall system security .The methodology is applied to a six bus Garver system The result obtained with the proposed method are validated with the results reported in the earlier research papers

Transmission and interconnection planning in power systems: Contributions to investment under uncertainty and cross-border cost allocation.

<p>Electricity transmission network investments are playing a key role in the integration process of power systems in the European Union. Given the magnitude of investment costs, their irreversibility, and their impact in the overall development of a region, accounting for the role of uncertainties as well as the involvement of multiple parties in the decision process allows for improved and more robust investment decisions. Even though the creation of this internal energy market requires attention to flexibility and strategic decision-making, existing literature and practitioners have not given proper attention to these topics. Using portfolios of real options, we present two stochastic mixed integer linear programming models for transmission network expansion planning. We study the importance of explicitly addressing uncertainties, the option to postpone decisions and other sources of flexibility in the design of transmission networks. In a case study based on the Azores archipelago we show how renewables penetration can increase by introducing contingency planning into the decision process considering generation capacity uncertainty. We also present a two-party Nash-Coase bargaining transmission capacity investment model. We illustrate optimal fair share cost allocation policies with a case study based on the Iberian market. Lastly, we develop a new model that considers both interconnection expansion planning under uncertainty and cross-border cost allocation based on portfolios of real options and Nash-Coase bargaining. The model is illustrated using Iberian transmission and market data.</p

Power transmission planning using heuristic optimisation techniques: Deterministic crowding genetic algorithms and Ant colony search methods

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The goal of transmission planning in electric power systems is a robust network which is economical, reliable, and in harmony with its environment taking into account the inherent uncertainties. For reasons of practicality, transmission planners have normally taken an incremental approach and tended to evaluate a relatively small number of expansion alternatives over a relatively short time horizon. In this thesis, two new planning methodologies namely the Deterministic Crowding Genetic Algorithm and the Ant Colony System are applied to solve the long term transmission planning problem. Both optimisation techniques consider a 'green field' approach, and are not constrained by the existing network design. They both identify the optimal transmission network over an extended time horizon based only on the expected pattern of electricity demand and generation sources. Two computer codes have been developed. An initial comparative investigation of the application of Ant Colony Optimisation and a Genetic Algorithm to an artificial test problem has been undertaken. It was found that both approaches were comparable for the artificial test problem.EPRSC and National Grid Company pl