Consensus-based approach to peer-to-peer electricity markets with product differentiation

With the sustained deployment of distributed generation capacities and the more proactive role of consumers, power systems and their operation are drifting away from a conventional top-down hierarchical structure. Electricity market structures, however, have not yet embraced that evolution. Respecting the high-dimensional, distributed and dynamic nature of modern power systems would translate to designing peer-to-peer markets or, at least, to using such an underlying decentralized structure to enable a bottom-up approach to future electricity markets. A peer-to-peer market structure based on a Multi-Bilateral Economic Dispatch (MBED) formulation is introduced, allowing for multi-bilateral trading with product differentiation, for instance based on consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is described to solve the MBED in fully decentralized manner. A set of realistic case studies and their analysis allow us showing that such peer-to-peer market structures can effectively yield market outcomes that are different from centralized market structures and optimal in terms of respecting consumers preferences while maximizing social welfare. Additionally, the RCI solving approach allows for a fully decentralized market clearing which converges with a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System

An economic evaluation of the potential for distributed energy in Australia

Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) recently completed a major study investigating the value of distributed energy (DE; collectively demand management, energy efficiency and distributed generation) technologies for reducing greenhouse gas emissions from Australia’s energy sector (CSIRO, 2009). This comprehensive report covered potential economic, environmental, technical, social, policy and regulatory impacts that could result from the wide scale adoption of these technologies. In this paper we highlight the economic findings from the study. Partial Equilibrium modeling of the stationary and transport sectors found that Australia could achieve a present value welfare gain of around $130 billion when operating under a 450 ppm carbon reduction trajectory through to 2050. Modeling also suggests that reduced volatility in the spot market could decrease average prices by up to 12% in 2030 and 65% in 2050 by using local resources to better cater for an evolving supply-demand imbalance. Further modeling suggests that even a small amount of distributed generation located within a distribution network has the potential to significantly alter electricity prices by changing the merit order of dispatch in an electricity spot market. Changes to the dispatch relative to a base case can have both positive and negative effects on network losses.Distributed energy; Economic modeling; Carbon price; Electricity markets

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Foresighted Demand Side Management

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities

Chance-Constrained Equilibrium in Electricity Markets With Asymmetric Forecasts

We develop a stochastic equilibrium model for an electricity market with asymmetric renewable energy forecasts. In our setting, market participants optimize their profits using public information about a conditional expectation of energy production but use private information about the forecast error distribution. This information is given in the form of samples and incorporated into profit-maximizing optimizations of market participants through chance constraints. We model information asymmetry by varying the sample size of participants' private information. We show that with more information available, the equilibrium gradually converges to the ideal solution provided by the perfect information scenario. Under information scarcity, however, we show that the market converges to the ideal equilibrium if participants are to infer the forecast error distribution from the statistical properties of the data at hand or share their private forecasts

Designing a Robust Decentralized Energy Transactions Framework for Active Prosumers in Peer-to-Peer Local Electricity Markets

In this paper, a fully decentralized local energy market based on peer-to-peer(P2P) trading is proposed for small-scale prosumers. In the proposed market, the prosumers are classified as buyers and sellers and can bilaterally engage in energy trading (P2P) with each other. The buyer prosumers are equipped with electrical storage and can participate in a demand response (DR) program while protecting their privacy. In addition to bilateral negotiating with the local sellers, these players can compensate for their energy deficiency from the upstream market as the retail market at hours without local generation. In this paper, the retail market price is assumed uncertain. Robust optimization is applied to model this uncertainty in the buyer prosumers model. The proposed decentralized robust optimization guarantees the solution’s existence for each realization of uncertainty components. Furthermore, it performs optimization to realize the hard worse case from uncertainty components. A fully decentralized approach known as the fast alternating direction method of multipliers (FADMM) is employed to solve the proposed decentralized robust problem. The proposed approach does not require third-party involvement as a supervisory node nor disclose the players’ private information. Numerical studies were carried out on a small distribution system with several prosumers. The numerical results suggested the operationality and applicability of the proposed decentralized robust framework and the decentralized solving method

Blockchain electricity trading using tokenised power delivery contracts. ESRI Working Paper No. 649 December 2019

This paper proposes a new mechanism for forward selling renewable electricity generation. In this transactive framework, a wind or solar farm may directly sell to consumers a claim on their future power output in the form of nonfungible blockchain tokens. Using the flexibility of smart contract code, which executes irrevocably on a blockchain, the realised generation levels will offset the token holders’ electricity consumption in near real-time. To elucidate the flexibility offered by such smart contracts, two ways of structuring these power delivery instruments are considered: firstly, an exotic tranched system, where more senior tokens holders enjoy priority claims on power, as compared against a simpler pro-rata scheme, where the realised output of a generator is equally apportioned between token holders. A notional market simulation is provided to explore whether, for instance, consumers could exploit the flatter power delivery profiles of more senior tranches to better schedule their responsive demands