Predictive power dispatch through negotiated locational pricing

A predictive mechanism is proposed in order to reduce price volatility linked to large fluctuations from de- mand and renewable energy generation in competitive electricity markets. The market participants are modelled as price-elastic units, price-inelastic units, and storage operators. The distributed control algorithm determines prices over a time horizon through a negotiation procedure in order to maximize social welfare while satisfying network constraints. A simple flow allocation method is used to assign responsibility for constraint violations on the network to individual units and a control rule is then used to adjust nodal prices accordingly. Such a framework is appropriate for the inclusion of aggregated household appliances or other ‘virtual’ market participants realized through smart grid infrastructure. Results are examined in detail for a 4-bus network and then success is demonstrated for a densely-populated 39-bus network. Formal convergence requirements are given under a restricted subset of the demonstrated conditions. The scheme is shown to allow storage to reduce price volatility in the presence of fluctuating demand

Insurance mechanisms for the reliability of electricity supply

In the context of rapid shifts in the energy supply mix and the onset of climate change, tail risk in power systems presents an emergent threat to system reliability. Flexible resources like load control, storage and distributed energy resources are potent tools to alleviate system strains during extreme events. However, market participants need appropriate economic incentives to exploit the value of such resources. While spot prices serve as robust indicators of real-time scarcity, a complex challenge lies in translating short-term signals to long-term investment decisions. This is especially pertinent in the context of markets marked by incompleteness, and agents with pronounced aversion to risk. The financial technology of insurance is targeted at the assessment, pricing, and management of extreme and catastrophic risks. This thesis proposes the novel application of insurance contracts and risk architectures to modern electricity markets, extending existing approaches to reliability risk management. This leads to the central research question of this thesis: Can the delivery of electricity service to consumers be made more reliable through the application of insurance mechanisms? The thesis investigates this question through three main streams of research: This first stream proposes the novel application of insurance contracts and capital reserving frameworks on the procurement of strategic reserves in electricity markets. A strategic reserve is a reliability mechanism in electricity markets that seeks to contract generation capacity incremental to that incentivised by short-term spot markets, for use in times of critical supply shortage. The insurance contracts allow consumers to elect differentiated reliability preferences, and align the financial interests of the insurer with such preferences. Application to a case study suggests the potential for improved consumer and social welfare while maintaining insurer viability and solvency. The design is also robust to non-transparent market parameters such as generator risk aversion. The second stream develops a locational insurance model to value resilience in power systems exposed to high-impact low-probability common-mode events. It is demonstrated that the implementation of this scheme in a large-scale power system could reduce load losses via investment in resilient distributed energy resources. However the cost of such insurance may be expensive, and appropriate calibration of consumer expectations and preferences is important. The final stream examines the interaction between the design of contracts between central agencies and storage resources, and the operation of the resources in the market. Five principles for central agency contracting are proposed, focusing on incentive compatibility with existing spot dispatch and limiting distortions to long-term hedging markets. The principles are applied specifically to contracts with storage resources. It is demonstrated that many early designs for storage auctions may be inconsistent with the identified principles. A novel storage contract ‘yardstick’ is proposed, which is shown to align participant dispatch incentives, while maintaining revenue support

Self-organizing Coordination of Multi-Agent Microgrid Networks

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.Dissertation/ThesisDoctoral Dissertation Systems Engineering 201

Multiagent System Architecture for Short-term Operation of Integrated Microgrids

The forthcoming smart grids are comprised of integrated microgrids operating in grid-connected and isolated mode with local generation, storage and demand response (DR) programs. The proposed model is based on three successive complementary steps for power transaction in the market environment. The first step is characterized as a microgrid’s internal market; the second concerns negotiations between distinct interconnected microgrids; and finally, the third refers to the actual electricity market. The proposed approach is modeled and tested using a MAS framework directed to the study of the smart grids environment, including the simulation of electricity markets. This is achieved through the integration of the proposed approach with the MASGriP (Multi-Agent Smart Grid Platform) system

A Decentralised Transactive Energy Market Considering Physical System Constraints

Increasing levels of Distributed Energy Resources (DERs) are expected to play a key role in achieving global electricity decarbonisation goals, providing both a challenge and an opportunity for the electricity industry. Conventional approaches such as Net Energy Metering (NEM) have been questioned regarding their effectiveness in properly rewarding DERs, and larger efforts around the integration of DERs into wholesale markets do not address potential value streams at the distribution system level. Local energy markets leveraging direct Peer-to-Peer (P2P) trading have been proposed as a solution, which can increase prosumer participation in lower cost and more reliable supply of energy to consumers. Many approaches have been proposed to determine the optimal dispatch of distributed resources; however, a gap remains in the research to date on how to efficiently allow for prosumer decision autonomy while ensuring that the physical layer of the power system is considered. This thesis proposes a decentralised transactive solution that retains prosumer negotiation and decision autonomy, while using network operator and market determined prices to allocate limited system resources for a feasible, locally optimal system state. Peer-to-Utility (P2U) transactions are added to existing P2P energy frameworks to obtain transactive local peer decision criteria considering Peer-Centric (PC) and System-Centric (SC) objectives. Peers are able to interact with wholesale electricity market derived prices through P2U transactions, allowing for consideration of net export value in welfare maximising decisions. The proposed approach includes a split transaction fee pricing mechanism for virtual prosumer interactions that considers the networks characteristics such as topology and operational constraints to ensure consideration of the physical layer in peer decision making. In addition to pricing mechanisms for coupling the virtual and physical layers, a congestion clearing process is proposed, which coordinates with the decentralised transaction matching process and the Network Usage Charges (NUCs) to ensure efficient allocation of network capacity. Previously reported distribution networks are used to compare the transaction decisions, economic performance, and system performance of the proposed solution with existing approaches. The results demonstrate the effectiveness of the proposed method in ensuring system feasible, locally optimal transaction sets with prioritisation of local peers

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

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio