Mechanism design for information aggregation within the smart grid

The introduction of a smart electricity grid enables a greater amount of information exchange between consumers and their suppliers. This can be exploited by novel aggregation services to save money by more optimally purchasing electricity for those consumers. Now, if the aggregation service pays consumers for said information, then both parties could benefit. However, any such payment mechanism must be carefully designed to encourage the customers (say, home-owners) to exert effort in gathering information and then to truthfully report it to the aggregator. This work develops a model of the information aggregation problem where each home has an autonomous home agent, which acts on its behalf to gather information and report it to the aggregation agent. The aggregator has its own historical consumption information for each house under its service, so it can make an imprecise estimate of the future aggregate consumption of the houses for which it is responsible. However, it uses the information sent by the home agents in order to make a more precise estimate and, in return, gives each home agent a reward whose amount is determined by the payment mechanism in use by the aggregator. There are three desirable properties of a mechanism that this work considers: budget balance (the aggregator does not reward the agents more than it saves), incentive compatibility (agents are encouraged to report truthfully), and finally individual rationality (the payments to the home agents must outweigh their incurred costs). In this thesis, mechanism design is used to develop and analyse two mechanisms. The first, named the uniform mechanism, divides the savings made by the aggregator equally among the houses. This is both Nash incentive compatible, strongly budget balanced and individually rational. However, the agents' rewards are not fair insofar as each agent is rewarded equally irrespective of that agent's actual contribution to the savings. This results in a smaller incentive for agents to produce precise reports. Moreover, it encourages undesirable behaviour from agents who are able to make the loads placed upon the grid more volatile such that they are harder to predict. To resolve these issues, a novel scoring rule-based mechanism named sum of others' plus max is developed, which uses the spherical scoring rule to more fairly distribute rewards to agents based on the accuracy and precision of their individual reports. This mechanism is weakly budget balanced, dominant strategy incentive compatible and individually rational. Moreover, it encourages agents to make their loads less volatile, such that they are more predictable. This has obvious advantages to the electricity grid. For example, the amount of spinning reserve generation can be reduced, reducing the carbon output of the grid and the cost per unit of electricity. This work makes use of both theoretical and empirical analysis in order to evaluate the aforementioned mechanisms. Theoretical analysis is used in order to prove budget balance, individual rationality and incentive compatibility. However, theoretical evaluation of the equilibrium strategies within each of the mechanisms quickly becomes intractable. Consequently, empirical evaluation is used to further analyse the properties of the mechanisms. This analysis is first performed in an environment in which agents are able to manipulate their reports. However, further analysis is provided which shows the behaviour of the agents when they are able to make themselves harder to predict. Such a scenario has thus far not been discussed within mechanism design literature. Empirical analysis shows the sum of others' plus max mechanism to provide greater incentives for agents to make precise predictions. Furthermore, as a result of this, the aggregator increases its utility through implementing the sum of others' plus max mechanism over the uniform mechanism and over implementing no mechanism. Moreover, in settings which allow agents to manipulate the volatility of their loads, it is shown that the uniform mechanism causes the aggregator to lose utility in comparison to using no mechanism, whereas in comparison to no mechanism, the sum of others' plus max mechanism causes an increase in utility to the aggregator

An Architecture for Distributed Energies Trading in Byzantine-Based Blockchain

With the development of smart cities, not only are all corners of the city connected to each other, but also connected from city to city. They form a large distributed network together, which can facilitate the integration of distributed energy station (DES) and corresponding smart aggregators. Nevertheless, because of potential security and privacy protection arisen from trustless energies trading, how to make such energies trading goes smoothly is a tricky challenge. In this paper, we propose a blockchain-based multiple energies trading (B-MET) system for secure and efficient energies trading by executing a smart contract we design. Because energies trading requires the blockchain in B-MET system to have high throughput and low latency, we design a new byzantine-based consensus mechanism (BCM) based on node's credit to improve efficiency for the consortium blockchain under the B-MET system. Then, we take combined heat and power (CHP) system as a typical example that provides distributed energies. We quantify their utilities, and model the interactions between aggregators and DESs in a smart city by a novel multi-leader multi-follower Stackelberg game. It is analyzed and solved by reaching Nash equilibrium between aggregators, which reflects the competition between aggregators to purchase energies from DESs. In the end, we conduct plenty of numerical simulations to evaluate and verify our proposed model and algorithms, which demonstrate their correctness and efficiency completely

Scalable Online Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators

In this work, a Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers. This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers' desired daily energy demand is met. Then, a scalable (with the number of prosumers) approach is proposed to find approximate equilibria based on online sampling and learning of the prosumers' cumulative best response. Moreover, bounds are provided on the quality of the approximate equilibrium solution. Last, real-world data from the California day-ahead energy market and the University of California at Davis building energy demands are utilized to demonstrate the efficacy of the proposed framework and the online scalable solution.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl