Congestion Management using Local Flexibility Markets: Recent Development and Challenges

Increasing amount of renewable based distributed generation at distribution systems, leads to an increased need for active distribution network management dealing with local network congestion and voltage issues. Development of local flexibility markets aims to provide a market-based solution to these issues. This paper presents a comprehensive review of proposed approaches towards markets exploiting the flexibilities from the demand-side. Efforts have been made on presenting a systematic overview of market design, including e.g. framework, participation, bidding and clearing mechanisms, of local flexibility market proposals developed in recent years. The implementation and regulatory issues and challenges are also discussed. The paper also presents the conceptual framework of the local flexibility service market which is currently being developed within UNITED-GRID project. This proposal aims to provide a holistic approach on local service markets, so that Distribution System Operators (DSOs) are provided with a market-based instrument to manage their networks efficiently

Demand response business model canvas: A tool for flexibility creation in the electricity markets

Game to promote energy efficiency action

Evaluation of Four European Local Flexibility Market Operators with the aim of Reducing Grid Investment Through Reinforcements

Over the years, more distributed generation based on renewable energy sources are added to the distribution networks, therefore, active distribution network management is necessary to address local network congestion and voltage issues. Congestion control is one of the most hopeful approaches to resolving network issues among the diverse options. Traditionally, the transmission grid has been the level at which congestion management systems have been managed. However, the control method would have to be extended to the distribution network as well due to the widespread use of Distributed Generators (DGs) and the predicted harsh loading situations. Recently, scholars and others working in the electric grid industry have been more interested in strategies for reducing congestion in distribution networks and non-market-based approaches have been proposed. However, it might not be the best and most economical choice depending on the use case scenario. This paper presents an evaluation of four local flexibility market operators with the aim of reducing investment on the grid through network reinforcement. While network reinforcement is expensive, it is often the first option used by DSOs to address congestion issues since DSOs have done it repeatedly and are technically capable of doing so. In addition, reinforcement is a trustworthy solution. To address these problems based on the market, local flexibility markets are being developed. Flexibility markets are an effective strategy for maximizing the efficiency of the current distribution grids. Piclo Flex, Enera, GOPACS, and NODES are four innovative initiatives that incorporate flexibility markets that we evaluated in this thesis. There are differences amongst the projects in terms of the degree to which the flexibility markets are integrated into other existing markets, the quality of flexible service, the third-party market operators, the use of standardized commodities, and grid rules that require TSO-DSO/DSO-DSO coordination. Because each project has a unique vision, set of use cases, or level of project maturity, the answers to these questions vary. Our case study examination of the four ground-breaking projects will help the reader to understand the local flexibility markets and identify the most promising flexibility market operator to use when we are trying to postpone or defer network reinforcement which in turn reduces the investment on the grid

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities