Network aware local flexibility markets : Business case analysis

The power system is undergoing a period of transformation towards a cleaner, more resilient, and just paradigm based on renewable energy and distributed energy resources. The period of transition from a fossil-fuel based system towards a renewable-based one is full of opportunities, but also challenges. The role of distribution grids in this new paradigm will change due to an increased share of renewable generation assets connected to them, and less predictable load profiles. Active management of distribution grids is not an option anymore, but a necessity to maintain a safe and reliable power system. This thesis work is focused on exploring on such method of active management of the distribution grids using the flexibility from multiple sources available via network aware flexibility market. The particular focus of the work is on exploring the business model of a network aware local flexibility market. Local flexibility markets are one of the options that distribution system operators are exploring to actively manage their grids. This thesis research the business model impact caused by the addition of network aware capabilities into (local) market clearing algorithms. Therefore, it studies through a business case analysis how this innovative solution influences market dynamics (and market participants), and it also studies the impact on the business model of the market operator. Two complementary approaches have been used to evaluate the impact on market dynamics and the impact on the business model. First, for the market dynamics a business case study has been performed. On it, market clearing events have been simulated using the algorithms developed by DTU. Second, for the business model impact analysis the results of the business case have been combined with industry insights gained during discussions along the development of the thesis. From the business case analysis, the main conclusions are that: with enough liquidity in the market the network aware algorithms can perform as good as their non-network aware counterparts. Therefore, on average, market participants will not be affected by the implementation of network aware algorithms. From the market operator perspective, it is true that the use of network aware algorithms requires higher computational power, but this could be expected since the algorithms have added features. When it comes to the business modelling work, interesting discoveries have been made. The implementation of network aware algorithms for market clearing has a direct impact on the market operator business model. The new network aware market operator will have to perform new tasks as: (confidential) data collection and storage, and possibly new roles and responsibilities will be attributed to it. One of the main uncertainties of the business model according to the industrial partners consulted is the liability over failed market clearings. Up until now if the security and quality of supply were bad, the system operator was responsible for that. With network aware local markets, such responsibility is blurred between the system operator and the market operator. In this thesis the study of the business model for a network aware local flexibility market has been performed, giving relevant insights on the opportunities and challenges such business idea entails. Furthermore, during the development of the research work future topics for research have been presented to further explore new business models for local market operator

A Risk-Based Decision Framework for the Distribution Company in Mutual Interaction with the Wholesale Day-ahead Market and Microgrids

One of the emergent prospects for active distribution networks ( DN ) is to establish new roles to the distribution company ( DISCO ). The DISCO can act as an aggregator of the resources existing in the DN , also when parts of the network are structured and managed as microgrids ( MG s). The new roles of the DISCO may open the participation of the DISCO as a player trading energy in the wholesale markets, as well as in local energy markets. In this paper, the decision making aspects involving the DISCO are addressed by proposing a bilevel optimization approach in which the DISCO problem is modeled as the upper-level problem and the MG s problems and day-ahead wholesale market clearing process are modeled as the lower-level problems. To include the uncertainty of renewable energy sources, a risk-based two-stage stochastic problem is formulated, in which the DISCO 's risk aversion is modeled by using the conditional value at risk. The resulting nonlinear bilevel model is transformed into a linear single-level one by applying the Karush–Kuhn–Tucker conditions and the duality theory. The effectiveness of the model is shown in the application to the IEEE 33-bus DN connected to the IEEE RTS 24-bus power system

Monopolistic and game-based approaches to transact energy flexibility

The appearance of the flexible behavior of end-users based on demand response programs makes the power distribution grids more active. Thus, electricity market participants in the bottom layer of the power system, wish to be involved in the decision-making process related to local energy management problems, increasing the efficiency of the energy trade in distribution networks. This paper proposes monopolistic and game-based approaches for the management of energy flexibility through end-users, aggregators, and the Distribution System Operator (DSO) which are defined as agents in the power distribution system. Besides, a 33-bus distribution network is considered to evaluate the performance of our proposed approaches for energy flexibility management model based on impact of flexibility behaviors of end-users and aggregators in the distribution network. According to the simulation results, it is concluded that although the monopolistic approach could be profitable for all agents in the distribution network, the game-based approach is not profitable for end-users.©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

A Hybrid Incentive Program for Managing Electric Vehicle Charging Flexibility

With the mass roll-out of electric vehicles (EVs) and rapid progress in battery technology, utilizing EV charging flexibility has become a promising solution for supporting economic and secured power system operations. This work proposes a novel hybrid incentive program, which encourages EV owners to sell their charging flexibility to a charging station (CS) and achieve a win-win situation for both EV owners and the CS. Unlike existing approaches, the proposed hybrid incentive program is simultaneously featured with simplicity, consistency, and controllability. To determine the incentive payment parameters, an optimal incentive price selection model is developed. In the solution methodology, we first linearize the original problem, then develop an adaptive ADMM algorithm to efficiently solve the formulated problem. Case studies confirm the superiority of the proposed hybrid incentive program over the state-of-the-arts, achieving 22.51% of EV owners’ cost reduction, 31.18% of energy market bill reduction, and 64.13% of potential charging flexibility utilization

A review of co-optimization approaches for operational and planning problems in the energy sector

This paper contributes to a comprehensive perspective on the application of co-optimization in the energy sector – tracking the frontiers and trends in the field and identifying possible research gaps – based on a systematic literature review of 211 related studies. The use of co-optimization is addressed from a variety of perspectives by splitting the studies into ten key categories. Research has consistently shown that co-optimization approaches can be technically challenging and it is usually a data-intensive procedure. Overall, a set of techniques such as relaxation, decomposition and linear approaches have been proposed for reducing the inherent nonlinear model's complexities. The need to coordinate the necessary data from multiples actors might increase the complexity of the problem since security and confidentiality issues would also be put on the table. The evidence from our review seems to suggest a pertinent role for addressing real-case systems in future models instead of using theoretical test cases as considered by most studies. The identified challenges for future co-optimization models include (i) dealing with the treatment of uncertainties and (ii) take into account the trade-offs among modelling fidelity, spatial granularity and geographical coverage. Although there is also a growing body of literature that recognizes the importance of co-optimization focused on integrating supply and demand-side options, there has been little work in the development of co-optimization models for long-term decision-making, intending to recognize the impact of short-term variability of both demand and RES supply and well suited to systems with a high share of RES and under different demand flexibility conditions. The research results represent a further step towards the importance of developing more comprehensive approaches for integrating short-term constraints in future co-optimized planning models. The findings provide a solid evidence base for the multi-dimensionality of the co-optimization problems and contriThis work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT – Fundaça˜o para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020