Rewarding Cooperative Virtual Power Plant Formation Using Scoring Rules 1

Abstract Virtual Power Plants (VPPs) are fast emerging as a viable means of integrating small and distributed energy resources (DERs), like wind and solar, into the electricity supply network (Grid). VPPs are formed via the aggregation of a large number of DERs, so that they exhibit the characteristics of a traditional generator in terms of predictability and robustness. In this work, we promote the formation of such "cooperative" VPPs (CVPPs) using techniques from the field of distributed Artificial Intelligence and game theory. In particular, we design a payment mechanism that encourages DERs to join CVPPs with increased size and visibility to the network operator. Our method is based on strictly proper scoring rules and incentivises the provision of accurate predictions of expected electricity generation from member DERs, which aids in the planning of the supply schedule at the Grid. We empirically evaluate our approach using the real-world setting of 16 commercial wind farms in the UK, and we show that it incentivises real DERs to form CVPPs, and outperforms the current state of the art payment mechanism developed for this problem

Cooperatives for demand side management

We propose a new scheme for efficient demand side management for the Smart Grid. Specifically, we envisage and promote the formation of cooperatives of medium-large consumers and equip them (via our proposed mechanisms) with the capability of regularly participating in the existing electricity markets by providing electricity demand reduction services to the Grid. Based on mechanism design principles, we develop a model for such cooperatives by designing methods for estimating suitable reduction amounts, placing bids in the market and redistributing the obtained revenue amongst the member agents. Our mechanism is such that the member agents have no incentive to show artificial reductions with the aim of increasing their revenue

Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

Building an Ethical Small Group (Chapter 9 of Meeting the Ethical Challenges of Leadership)

This chapter examines ethical leadership in the small-group context. To help create groups that brighten rather than darken the lives of participants, leaders must foster individual ethical accountability among group members, ensure ethical group interaction, avoid moral pitfalls, and establish ethical relationships with other groups. In his metaphor of the leader\u27s light or shadow, Parker Palmer emphasizes that leaders shape the settings or contexts around them. According to Palmer, leaders are people who have an unusual degree of power to create the conditions under which other people must live and move and have their being, conditions that can either be as illuminating as heaven or as shadowy as hell. 1 In this final section of the text, I\u27ll describe some of the ways we can create conditions that illuminate the lives of followers in small-group, organizational, global, and crisis settings. Shedding light means both resisting and exerting influence. We must fend off pressures to engage in unethical behavior while actively seeking to create healthier moral environments

Game-theoretic modeling of transmission line reinforcements with distributed generation

Favourable sites for renewable generation are often remote locations (such as islands) where installed capacity, e.g. from wind turbines, exceeds local aggregate demand. We study the effect that curtailment mechanisms - applied when there is excess generation - have on the incentives to build additional capacity and the profitability of the generators. Next, for a two-location setting, we study the combined effect that curtailment schemes and line access rules have on the decision to invest in transmission expansion. In particular, for "common access" rules, this leads to a Stackelberg game between transmission and local generation capacity investors, and we characterise the equilibrium of this game. Finally, we apply and exemplify our model to a concrete problem of a grid reinforcement project, between Hunterston and the Kintyre peninsula, in western Scotland, and we determine a mechanism for setting transmission charges that assures both the profitability of the line and local renewable investors

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