Agent-based control for decentralised demand side management in the smart grid

Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency)

Setting Fees in Competing Double Auction Marketplaces: An Equilibrium Analysis

In this paper, we analyse competing double auction marketplaces that vie for traders and need to set appropriate fees to make a profit. Specifically, we show how competing marketplaces should set their fees by analysing the equilibrium behaviour of two competing marketplaces. In doing so, we focus on two different types of market fees: registration fees charged to traders when they enter the marketplace, and profit fees charged to traders when they make transactions. In more detail, given the market fees, we first derive equations to calculate the marketplaces' expected profits. Then we analyse the equilibrium charging behaviour of marketplaces in two different cases: where competing marketplaces can only charge the same type of fees and where competing marketplaces can charge different types of fees. This analysis provides insights which can be used to guide the charging behaviour of competing marketplaces. We also analyse whether two marketplaces can co-exist in equilibrium. We find that, when both marketplaces are limited to charging the same type of fees, traders will eventually converge to one marketplace. However, when different types of fees are allowed, traders may converge to different marketplaces (i.e. multiple marketplaces can co-exist)

A Game-Theoretic Analysis of Market Selection Strategies for Competing Double Auction Marketplaces

In this paper, we propose a novel general framework for analysing competing double auction markets that vie for traders, who then need to choose which market to go to. Based on this framework, we analyse the competition between two markets in detail. Specifically, we game-theoretically analyse the equilibrium behaviour of traders' market selection strategies and adopt evolutionary game theory to investigate how traders dynamically change their strategies, and thus, which equilibrium, if any, can be reached. In so doing, we show that it is unlikely for these competing markets to coexist. Eventually, all traders will always converge to locating themselves at one of the markets. Somewhat surprisingly, we find that sometimes all traders converge to the market that charges higher fees. Thus we further analyse this phenomenon, and specifically determine the factors that affect such migration

Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers

This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns

A Market-based Approach to Multi-factory Scheduling

In this paper, we report on the design of a novel market-based approach for decentralised scheduling across multiple factories. Specifically, because of the limitations of scheduling in a centralised manner -- which requires a center to have complete and perfect information for optimality and the truthful revelation of potentially commercially private preferences to that center -- we advocate an informationally decentralised approach that is both agile and dynamic. In particular, this work adopts a market-based approach for decentralised scheduling by considering the different stakeholders representing different factories as self-interested, profit-motivated economic agents that trade resources for the scheduling of jobs. The overall schedule of these jobs is then an emergent behaviour of the strategic interaction of these trading agents bidding for resources in a market based on limited information and their own preferences. Using a simple (zero-intelligence) bidding strategy, we empirically demonstrate that our market-based approach achieves a lower bound efficiency of 84%. This represents a trade-off between a reasonable level of efficiency (compared to a centralised approach) and the desirable benefits of a decentralised solution

Deeper Hedging: A New Agent-based Model for Effective Deep Hedging

We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders, and volatility traders. The volatility traders participate in the market by innovatively following a Heston-style volatility signal. The proposed model generalises both the extended Chiarella model and the Heston stochastic volatility model, and is calibrated to reproduce as many empirical stylized facts as possible. According to the stylised facts distance metric, the proposed model is able to reproduce more realistic financial time series than three baseline models: the extended Chiarella model, the Heston model, and the Geometric Brownian Motion. The proposed model is further validated by the Generalized Subtracted L-divergence metric. With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels. The deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and is trained to maximize profits and minimize risks. Our testing results reveal that the deep hedging agent, trained with data generated by our proposed model, outperforms the baseline in most transaction cost levels. Furthermore, the testing process, which is conducted using empirical data, demonstrates the effective performance of the trained deep hedging agent in a realistic trading environment.Comment: Accepted in the 4th ACM International Conference on AI in Finance (ICAIF'23

Fairness in Power Flow Network Congestion Management with Outer Matching and Principal Notions of Fair Division

The problem of network flow congestion occurring in power networks is increasing in severity. Especially in low-voltage networks this is a novel development. The congestion is caused for a large part by distributed and renewable energy sources introducing a complex blend of prosumers to the network. Since congestion management solutions may require individual prosumers to alter their prosumption, the concept of fairness has become a crucial topic of attention. This paper presents a concept of fairness for low-voltage networks that prioritizes local, outer matching and allocates grid access through fair division of available capacity. Specifically, this paper discusses three distinct principal notions of fair division; proportional, egalitarian, and nondiscriminatory division. In addition, this paper devises an efficient algorithmic mechanism that computes such fair allocations in limited computational time, and proves that only egalitarian division results in incentive compatibility of the mechanism

The Structure and Behaviour of the Continuous Double Auction

The last decade has seen a shift in emphasis from centralised to decentralised systems to meet the demanding coordination requirements of today's complex computer systems. In such systems, the aim is to achieve effective decentralised control through autonomous software agents that perform local decision-making based on incomplete and imperfect information. Specifically, when the various agents interact, the system behaves as a computational ecology with no single agent coordinating their actions. In this thesis, we focus on one specific type of computational ecology, the Continuous Double Auction (CDA), and investigate market-oriented approaches to decentralised control. In particular, the CDA is a fixed-duration auction mechanism where multiple buyers and sellers compete to buy and sell goods, respectively, in the market, and where transactions can occur at any time whenever an offer to buy and an offer to sell match. Now, in such a market mechanism, the decentralised control is achieved through the decentralised allocation of resources, which, in turn, is an emergent behaviour of buyers and sellers trading in the market. The CDA was chosen, among the plenitude of auction formats available, because it allows efficient resource allocation without the need of a centralised auctioneer. Against this background, we look at both the structure and the behaviour of the CDA in our attempt to build an efficient and robust mechanism for decentralised control. We seek to do this for both stable environments, in which the market demand and supply do not change and dynamic ones in which there are sporadic changes (known as market shocks). While the structure of the CDA defines the agents' interactions in the market, the behaviour of the CDA is determined by what emerges when the buyers and sellers compete to maximise their individual profits. In more detail, on the structural aspect, we first look at how the market protocol of the CDA can be modified to meet desirable properties for the system (such as high market efficiency, fairness of profit distribution among agents and market stability). Second, we use this modified protocol to efficiently solve a complex decentralised task allocation problem with limited-capacity suppliers that have start-up production costs and consumers with inelastic demand. Furthermore, we demonstrate that the structure of this CDA variant is very efficient (an average of 80% and upto 90%) by evaluating the mechanism with very simple agent behaviours. In so doing, we emphasise the effect of the structure, rather than the behaviour, on efficiency. In the behavioural aspect, we first developed a multi-layered framework for designing strategies that autonomous agents can use for trading in various types of market mechanisms. We then use this framework to design a novel Adaptive-Aggressiveness (AA) strategy for the CDA. Specifically, our bidding strategy has both a short and a long-term learning mechanism to adapt its behaviour to changing market conditions and it is designed to be robust in both static and dynamic environments. Furthermore, we also developed a novel framework that uses a two-population evolutionary game theoretic approach to analyse the strategic interactions of buyers and sellers in the CDA. Finally, we develop effective methodologies for evaluating strategies for the CDA in both homogeneous and heterogeneous populations, within static and dynamic environments. We then evaluate the AA bidding strategy against the state of the art using these methodologies. By so doing, we show that, within homogeneous populations, the AA strategy outperformed the benchmarks, in terms of market efficiency, by up to 3.6% in the static case and 2.8% in the dynamic case. Within heterogeneous populations, based on our evolutionary game theoretic framework, we identify that there is a probability above 85% that the AA strategy will eventually be adopted by buyers and sellers in the market (for being more efficient) and, therefore, AA is also better than the benchmarks in heterogeneous populations as well

IAMwildCAT: The Winning Strategy for the TAC Market Design Competition

In this paper we describe the IAMwildCAT agent, designed for the TAC Market Design game which is part of the International Trading Agent Competition. The objective of an agent in this competition is to effectively manage and operate a market that attracts traders to compete for resources in it. This market, in turn, competes against markets operated by other competition entrants and the aim is to maximise the market and profit share of the agent, as well as its transaction success rate. To do this, the agent needs to continually monitor and adapt, in response to the competing marketplaces, the rules it uses to accept offers, clear the market, price the transactions and charge the traders. Given this context, this paper details IAMwildCAT’s strategic behaviour and describes the wide techniques we developed to operationalise this. Finally, we empirically analyse our agent in different environments, including the 2007 competition where it ranked first

An Equilibrium Analysis of Competing Double Auction Marketplaces using Fictitious Play

In this paper, we analyse how traders select marketplaces and bid in a setting with multiple competing marketplaces. Specifically, we use a fictitious play algorithm to analyse the traders' equilibrium strategies for market selection and bidding when their types are continuous. To achieve this, we first analyse traders' equilibrium bidding strategies in a single marketplace and find that they shade their offers in equilibrium and the degree to which they do this depends on the amount and types of fees that are charged by the marketplace. Building on this, we then analyse equilibrium strategies for traders in competing marketplaces in two particular cases. In the first, we assume that traders can only select one marketplace at a time. For this, we show that, in equilibrium, all traders who choose one of the marketplaces eventually converge to the same one. In the second case, we allow buyers to participate in multiple marketplaces at a time, while sellers can only select one marketplace. For this, we show that sellers eventually distribute in different marketplaces in equilibrium and that buyers shade less and sellers shade more in the equilibrium bidding strategy (since sellers have more market power than buyers)