Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle

This paper considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighboring TP(s), although the optimal investment decision of each TP is contingent upon those of the neighboring TPs. A game-theoretic interaction among the TPs does not necessarily lead to this overall social optimum. We, therefore, introduce a social planner and call it the Transmission Planning Coordinator (TPC) whose goal is to attain the optimal possible social welfare for the bigger geographical region. In order to achieve this goal, this paper introduces a new incentive mechanism, based on distributed optimization theory. This incentive mechanism can be viewed as a set of rules of the transmission expansion investment coordination game, set by the social planner TPC, such that, even if the individual TPs act selfishly, it will still lead to the TPC's goal of attaining overall social optimum. Finally, the effectiveness of our approach is demonstrated through several simulation studies

A Local Capacity Market Providing Local and System-wide Flexibility Services

A large amount of renewable energy sources and electric vehicles will be integrated into future electricity distribution and transmission systems. New flexibility services from distribution network are needed to manage the related challenges. This paper proposes a local flexible capacity market (LFCM) in the distribution network providing system-wide and local flexibility services for transmission (TSO) and distribution system operators (DSO). The TSO and the DSO play the role of buyers, whereas prosumers connected to the distribution network are the sellers. The LFCM consists of three stages. At the first stage, the offers of flexibility sellers are matched with the bids of flexibility buyers aiming to maximize the social welfare of all participants. At the second stage, the accepted flexible capacities are checked by the DSO not to violate the constraints of the local network. The third stage accepts the offers of the sellers based on the results of the previous stage. The results related to the chosen case study demonstrate that the local flexible resources can help the DSO control the voltage and manage periods of congestion. Besides, the owners of the resources can obtain revenues by selling flexibility services while improving electricity supply reliability.©2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0fi=vertaisarvioitu|en=peerReviewed

Probabilistic Multi-product Trading in Sequential Intraday and Frequency-Regulation Markets

With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived through a series of transformation techniques. Additionally, we present two reformulations that re-frame the problem as a mixed-integer linear programming problem with uncertain parameters. Various aspects of the model are thoroughly examined to observe the optimal multi-product trading behavior of hydro power plant assets, along with numerous case studies. Leveraging historical data from Nordic electricity markets, we construct realistic scenarios for the uncertain parameters. Furthermore, we then proposed an algorithm based on the No-U-Turn sampler to provide probability distribution functions of cleared prices in frequency-regulation and day-ahead markets. These distribution functions offer valuable statistical insights into temporal price risks for informed multi-product optimal-trading decisions.Comment: 10 pages, 9 figure

An optimal real-time pricing strategy for aggregating distributed generation and battery storage systems in energy communities: A stochastic bilevel optimization approach

The expansion of distributed electricity generation and the increasing capacity of installed battery storage systems at the community level have posed challenges to efficient technical and economic operation of the power systems. With advances in smart-grid infrastructure, many innovative demand response business models have sought to tackle these challenges, while creating financial benefits for the participating actors. In this context, we propose an optimal real-time pricing (ORTP) approach for the aggregation of distributed energy resources within energy communities. We formulate the interaction between a community-owned profit-maximizing aggregator and the users (consumers with electricity generation and storage potential, known as “prosumagers”, and electric vehicles) as a stochastic bilevel disjunctive program. To solve the problem efficiently, we offer a novel solution algorithm, which applies a linear quasi-relaxation approach and an innovative dynamic partitioning technique. We introduce benchmark tariffs and solution algorithms and assess the performance of the proposed pricing strategy and solution algorithm in four case studies. Our results show that the ORTP strategy increases community welfare while providing useful grid services. Furthermore, our findings reveal the superior computational efficiency of our proposed solution algorithm in comparison to benchmark algorithms

Transmission planning in liberalised electricity markets in the context of market power

Transmission planning is complex, involving consideration of the impact of a transmission augmentation under a large number of future demand and supply scenarios. In principle, the transmission planning problem is well understood in the context of a vertically-integrated electricity industry. In this context, a transmission augmentation has the following primary benefits: It allows for more efficient dispatch (allowing for lower cost remote generation to be used in place of higher cost local generation); it allows inefficient investment in generation to be deferred; and it reduces the need for operating reserves by allowing those reserves to be shared over a wider area. In principle, if the liberalised electricity market is sufficiently competitive, the same tools and techniques that have been developed for transmission planning in the context of an integrated electricity industry can be applied. However, two new issues arise: (a) The first is coordination between generation and transmission investment. How should transmission and generation investment be effectively coordinated? (b) The second issue is the problem of generator market power. Many commentators point out that electricity markets are prone to the exercise of market power. The additional benefits of reducing market power have been referred to as the 'competition benefit.' Although it is widely acknowledged that transmission investment may affect generator market power, there is as yet no widely accepted methodology for computing the competition benefits of a transmission augmentation and, in practice, competition benefits are only estimated on an ad hoc basis, if at all. This thesis sets out a methodology for modelling market power in the context of transmission planning. This methodology is based around a multi-level optimisation problem. The lowest level of this optimisation problem models the dispatch process in a liberalised electricity market, allowing for generator market power. The upper level of this optimisation problem models the behaviour of the transmission network service provider. In the next step, a numerical solution approach, termed the Hybrid Bi-Level Genetic Algorithm/Island Parallel Genetic Algorithm, HB GA/IPGA, was developed to find a good solution of the proposed structures. To further improve the performance of the Hybrid Bi-Level GA/IPGA, high performance computing techniques are employed. The main contributions of this research work are as follows; (1) A systematic modelling of generator market power in a liberalised electricity market through the concepts of simultaneous-move game and worst Nash equilibrium (2) Modelling of the interaction of a transmission network service provider and rival generating companies using a simultaneous-move game nested within a sequential-move game and tackling the multiple Nash equilibria problem through the concept of 'Stackelberg-Worst Nash equilibrium' (3) A game-theoretic framework for modelling the coordination of generation investment and transmission investment (4) A decomposition methodology for decomposing the total benefits of the transmission augmentation policies into the 'Efficiency Benefit', the 'Competition Benefit', and the 'Saving in generation investment cost' (5) The use of high performance computing technologies to improve the performance of the algorithm for solving the proposed constrained-optimisation problem ? in particular, using the 'Threads' model and 'Message Passing' model of parallel programming

A hybrid optimization approach for distribution capacitor allocation considering varying load conditions

This work presents a new algorithm based on a combination of fuzzy (FUZ), Forward Update (FWD), and Genetic Algorithm (GA) approaches for capacitor allocation in distribution feeders. The problem formulation considers three distinct objectives related to total cost of energy loss and total cost of capacitors including the purchase and installation costs and one term related to total cost of produced power under peak load condition. The novel formulation is a multi-objective and non-differentiable optimization problem. The proposed methodology of this article uses an iterative optimization technique based on Forward Update approach which is embedded in a Genetic Algorithm framework. The fuzzy reasoning supported by the fuzzy set theory is used for sitting of capacitors and the GA is employed for finding the optimum shape of membership functions. The proposed method has been implemented in a software package and its effectiveness has been verified through a 9-bus radial distribution feeder along with a 34-bus radial distribution feeder for the sake of conclusions supports. A comparison has been done among the proposed method of this paper and similar methods in other research works that shows the effectiveness of the proposed method of this paper for solving optimum capacitor planning problem

Multi-regional transmission planning as a non-cooperative decision-making

This paper discusses the transmission planning problem in a transmission network with multiple transmission planners. Each transmission planner is responsible for a region of the transmission network and maximizes its own region social welfare taking into account the transmission planning decisions of other transmission planners. The mathematical formulation of non-cooperative transmission planning problem is proposed. This problem is modeled using the multiple-leaders single-follower game in applied mathematics. The solution concept of the worst-Nash equilibrium is introduced to solve the set-up game. Different mathematical techniques are employed to formulate the worst-Nash equilibrium solution as a mixed-integer linear programming problem. A discussion on two possible applications of the derived mathematical structure is provided. The computational complexity is also discussed. The Three-Region IEEE-RTS96 example system is employed and modified to suit the purpose of analysis. The cooperative solution where the transmission planners merge into one single transmission planner is assumed as the benchmark in this study. The cooperative transmission planning is formulated in Appendix A of the paper. The results demonstrate the utility of the proposed solution in multi-regional transmission planning

Материаловедение и технология конструкционных материалов

This paper presents a hierarchical dispatch structure for efficient distribution network pricing. The dispatch coordination problem in the context of hierarchical network operators are addressed. We formulate decentralized generation dispatch into a bilevel optimization problem in which main network operator and the connected distribution network operator optimize their costs in two levels. By using Karush-Kuhn-Tucker conditions and Fortuny-Amat McCarl linearization, the bilevel optimization problem is reduced into a mixed-integer linear programming (MILP) problem. Equivalence between proposed hierarchical dispatch and centralized dispatch is proved. The model is solved in GAMS platform. IEEE 14-bus meshed network and IEEE 13-node radial network are connected to be an illustrative example offering numerical dispatch results. Three scenarios representing distributed generation (DGs) successive development stages are analyzed. Hierarchical dispatch achieves same results as traditional centralized dispatch in the three considered scenarios. Distribution network nodal prices are obtained. Intrinsic advantages of the proposed hierarchical dispatch are to reduce the dispatch complexity with increasing DGs penetration and provide distribution locational marginal prices (DLMP).QC 20160120</p