Distribution Network Planning and Operation With Autonomous Agents

With the restructured power system, different system operators and private investors are responsible for operating and maintaining the electricity networks. Moreover, with incentives for a clean environment and reducing the reliance on fossil fuel generation, future distribution networks adopt a considerable penetration of renewable energy sources. However, the uncertainty of renewable energy sources poses operational challenges in distribution networks. This thesis addresses the planning and operation of the distribution network with autonomous agents under uncertainty. First, a decentralized energy management system for unbalanced networked microgrids is developed. The energy management schemes in microgrids enhance the utilization of renewable energy resources and improve the reliability and resilience measures in distribution networks. While microgrids operate autonomously, the coordination among microgrid and distribution network operators contributes to the improvement in the economics and reliability of serving the demand. Therefore, a decentralized energy management framework for the networked microgrids is proposed. Furthermore, the unbalanced operation of the distribution network and microgrids, as well as the uncertainty in the operating modes of the microgrids, renewable energy resources, and demand, are addressed. The second research work presents a stochastic expansion planning framework to determine the installation time, location, and capacity of battery energy storage systems in the distribution network with considerable penetration of photovoltaic generation and data centers. The presented framework aims to minimize the capital cost of the battery energy storage and the operation cost of the distribution network while ensuring the security of energy supply for the data centers that serve end-users in the data network as well as the reliability requirements of the distribution network. The third research work proposes a coordinated expansion planning of natural gas-fired distributed generation in the power distribution and natural gas networks considering demand response. The problem is formulated as a distributionally robust optimization problem in which the uncertainties in the photovoltaic power generation, electricity load, demand bids, and natural gas demand are considered. The Wasserstein distance metric is employed to quantify the distance between the probability distribution functions. The last research work proposes a decentralized operation of the distribution network and hydrogen refueling stations equipped with hydrogen storage, electrolyzers, and fuel cells to serve hydrogen and electric vehicles. The uncertainties in the electricity demands, PV generation, hydrogen supply, and hydrogen demands are captured, and the problem is formulated as a Wasserstein distance-based distributionally robust optimization problem. The proposed framework coordinates the dispatch of the distributed generation in the distribution network with the hydrogen storage, electrolyzer, and fuel cell dispatch considering the worst-case probability distribution of the uncertain parameters. The proposed frameworks limit the information shared among these autonomous operators using Benders decomposition

Heuristics for Transmission Expansion Planning in Low-Carbon Energy System Models

Governments across the world are planning to increase the share of renewables in their energy systems. The siting of new wind and solar power plants requires close coordination with grid planning, and hence co-optimization of investment in generation and transmission expansion in spatially and temporally resolved models is an indispensable but complex problem. Particularly considerations of transmission expansion planning (TEP) add to the problem's complexity. Even if the power flow equations are linearized, the optimization problem is still bilinear and mixed-integer due to the dependence of line expansion on line impedance and a discrete set of line expansion options. While it is possible to linearize this mixed-integer nonlinear program (MINLP) by applying a big-M disjunctive relaxation, the resulting MILP is still hard to solve using state-of-the-art solvers for large-scale energy system models. In this paper we therefore develop heuristics to incorporate integer transmission expansion and responsive line impedances in energy system models, while retaining the lower computational effort of continuous linear programming (LP) by applying sequential linear programming (SLP) techniques, relaxation and discretization approaches. We benchmark their performance against the results of the exact formulation for a policy-relevant case study of the German transmission system in terms of their speed-up in computation time, deviation from optimal total system cost, and similarity of line expansion. Using heuristics we reduce computation times of joint generation and transmission optimisation by 82% with a maximal total system cost deviation of only 1.5%. The heuristics closely mirror optimal integer line investment of the exact MINLP with considerable time savings for policy-relevant low-carbon energy system optimization models.Comment: 8 pages, 7 figures, conference, EEM 201

Optimize Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon

Techno-economic assessment of energy storage systems in multi-energy microgrids utilizing decomposition methodology

Renewable resources and energy storage systems integrated into microgrids are crucial in attaining sustainable energy consumption and energy cost savings. This study conducts an in-depth analysis of diverse storage systems within multi-energy microgrids, including natural gas and electricity subsystems, with a comprehensive focus on techno-economic considerations. To achieve this objective, a methodology is developed, comprising an optimization model that facilitates the determination of optimal storage system locations within microgrids. The model considers various factors, such as operating and emission costs of both gas and electricity subsystems, and incorporates a sensitivity analysis to calculate the investment and maintenance costs associated with the storage systems. Due to the incorporation of voltage and current relations in the electricity subsystem as well as gas pressure and flow considerations in the natural gas subsystem, the developed model is classified as a mixed-integer nonlinear programming model. To address the inherent complexity in solving, a decomposition approach based on Outer Approximation/Equality Relaxation/Augmented Penalty is developed. This study offers scientific insights into the costs of energy storage systems, potential operational cost savings, and technical considerations of microgrid operation. The results of the developed decomposition approach demonstrate significant advantages, including reduced solving time and a decreased number of iterations

An Improved Mathematical Formulation For the Carbon Capture and Storage (CCS) Problem

abstract: Carbon Capture and Storage (CCS) is a climate stabilization strategy that prevents CO2 emissions from entering the atmosphere. Despite its benefits, impactful CCS projects require large investments in infrastructure, which could deter governments from implementing this strategy. In this sense, the development of innovative tools to support large-scale cost-efficient CCS deployment decisions is critical for climate change mitigation. This thesis proposes an improved mathematical formulation for the scalable infrastructure model for CCS (SimCCS), whose main objective is to design a minimum-cost pipe network to capture, transport, and store a target amount of CO2. Model decisions include source, reservoir, and pipe selection, as well as CO2 amounts to capture, store, and transport. By studying the SimCCS optimal solution and the subjacent network topology, new valid inequalities (VI) are proposed to strengthen the existing mathematical formulation. These constraints seek to improve the quality of the linear relaxation solutions in the branch and bound algorithm used to solve SimCCS. Each VI is explained with its intuitive description, mathematical structure and examples of resulting improvements. Further, all VIs are validated by assessing the impact of their elimination from the new formulation. The validated new formulation solves the 72-nodes Alberta problem up to 7 times faster than the original model. The upgraded model reduces the computation time required to solve SimCCS in 72% of randomly generated test instances, solving SimCCS up to 200 times faster. These formulations can be tested and then applied to enhance variants of the SimCCS and general fixed-charge network flow problems. Finally, an experience from testing a Benders decomposition approach for SimCCS is discussed and future scope of probable efficient solution-methods is outlined.Dissertation/ThesisMasters Thesis Industrial Engineering 201