Multi-agent planning

In computer science multi-agent planning involves coordinating the resources and activities of multiple agents. NASA says, "multiagent planning is concerned with planning by (and for) multiple agents. It can involve agents planning for a common goal, an agent coordinating the plans (plan merging) or planning of others, or agents refining their own plans while negotiating over tasks or resources. The topic also involves how agents can do this in real time while executing plans (distributed continual planning). Multiagent scheduling differs from multiagent planning the same way planning and scheduling differ: in scheduling often the tasks that need to be performed are already decided, and in practice, scheduling tends to focus on algorithms for specific problem domains". See also *Automated planning and scheduling *Distributed artificial intelligence *Cooperative distributed problem solving and Coordination *Multi-agent systems and Software agent and Self-organization *Multi-a

Multi-agent modelling of electricity markets

Abdelaziz Lahlou

This thesis describes the development of three conceptual models built to serve as decision support tools in liberalised electricity markets. The introduction of competition, higher uncertainty and decentralised planning requires new planning and analysis tools on the medium to long term to support decision making at the level of the industry as well as at the level of the market authorities. We have mainly developed models of market simulation and investment decision by investigating how multi-agent systems can contribute to the understanding of the decentralised nature of competitive decision making in a liberalised electricity market. The thesis focused particularly on bilateral transactions arguing that such transaction processes are specially relevant from the point of view of risk management when considering medium to long term decisions (for both portfolio management and investment decision making). We have used decision analysis under uncertainty and dynamic modeling approaches under a multi-agent framework by considering that such approaches are more adequate in a competitive environment due to the market actors' exposure to risks and need for adaptation in a changing environment. Monthly spot market prices are taken into account through scenarios that reproduce volatility by lognormal processes. Monte Carlo simulation is used to quantify the risks among which the spot prices are taken as the most significant source of uncertainty. The main objective of this thesis is to contribute to the problem of modeling investment decisions in new generation capacities using electricity market simulation from a planning point of view. This global objective is driven according to the: Modeling of the bilateral transaction processes between generation companies and demand companies at the level of wholesale market by taking into account the possible tradeoffs between spot and bilateral markets. The transaction processes models represent the market simulation modules by: Distinguishing the different forms the transaction processes can take (negotiation vs. auctions). Modeling of the market actors' portfolio management from a planning perspective by taking into account the market uncertainties (sales portfolio for generation companies and supply portfolios for demand companies). Modeling of the investment decisions in new electricity generation power plants. "Negotiation based portfolio management in a liberalised electricity market: a multiagent approach" The first model of the thesis formalizes the transactions of demand companies that would have to face a single generation company. The transaction process is here modeled as a bilateral negotiation algorithm between buyers and a seller. The negotiation is driven on two issues: prices and quantities of monthly contracts. It is simulated as a succession of offers and counteroffers during a limited period of time. Each market actor is searching for efficient transactions that satisfy its objectives, preferences and constraints. The reasoning process of the agents is based on the multi-attribute tradeoffs under uncertainty seeking each month the optimal configuration of portfolios composed of bilateral and spot contracts. The simulated output of the negotiation does not necessarily optimise the social welfare as both parties have opposing interests and interact under incomplete information. The efficiency of the algorithm is characterized by a ratio relatively to the Pareto optimal contracts. It was found that a supplier with nuanced risk aversion have high efficiency beyond the Pareto optimal points (between 100 and 120%). It indicates that the single generation company has a higher bargaining power than the individual demand companies independently of the preferences and negotiation behaviour of its counterparts. The negotiation efficiency of the different demand companies ranges from 80 to 100% and is related to their attitude toward risks. Risk averse demand companies accept higher risk premiums in order to avoid spot market risk exposure unlike other agents that may have more nuanced risk preferences. The bilateral contracts concluded covers the electricity demand partially or totally depending on the risk attitude. Demand companies with nuanced risk aversion negotiate contracts in order to manage some flexibility and take benefit from potential spot market opportunities. "Bilateral transactions in a competitive electricity market: multiple auctions using an agent based approach" The second transaction process models bilateral transactions between demand companies and several generation companies through multiple auctions. In this context, the generation companies must compete between each other to satisfy the call-for-bids (CfBs) launched simultaneously by each demand company, i.e. each buyer in the system opens an auction by expressing its needs and preferences by product (base and peak). After receiving the CfBs, the suppliers send offers responding to the multiple auctions opened by the demand companies. The offers timing is sequenced by a priority list that ranks the demand companies according to their value. The construction of the offers is modeled with a function of the evaluated utility of the customer based on its CfB. The demand companies, at the closure of their individual auction, compare and select the offers with maximum value by including the evaluation of the possible tradeoffs between bilateral and spot markets. The dynamic of competition requires the suppliers to adapt their monthly pricing strategies to satisfy their profit and market share objectives. The adaptive behaviour of the generation companies is here modeled using a directed learning approach. Learning is performed on the basis of two variables (risk premiums and market shares) which previous month instances permit causal inferences on eight rules of pricing parameters adaptation. The results of a case study show that the agents in the system build flexible portfolios (partial or full supply contracts, supply portfolio with several producers, spot contracts ...). The agents learning process tend to satisfy their objectives of profit and market shares. The bilateral market transaction volume represents between 70 and 90% of the total transaction volume (10 to 30% of spot contracts) as a result of the tradeoffs between the two markets. "Electricity generation capacity expansion under competition: A multi-agent dynamic programming model" The last model proposed in this thesis intends to model the decentralized investment decision making in a competitive electricity market. The capacity expansion model proposed aims at evaluating the medium to long term investment decisions according to criteria of risks and profits maximisation. We use the agent-based multiple-auction module to simulate he electricity market. The investment decisions of each supplier agent in the system are assessed according to a dynamic programming algorithm that explores different potential expansion plans in predefined periods. We consider that capital rationing, preservation of capital under risk and market share targeted ranges are three constraints that bound the expansion plans. Two major sources of uncertainties are considered: market price risk and strategic risk. Strategic risks are evaluated through a customer and competitors focus by putting into perspective the investment decision relatively to the competitors' potential expansion plans. Hence, the evaluation of an expansion plan is built as the convolution of the realizable incomes through the potential configurations of competitors strategic investment decisions. In our model, a supplier agent is seeking to take market positions relatively to its competitor through the market share that he can expect to secure its investment decisions. It appears that risk reduction highlighted here through bilateral transactions is a more realistic approach when assessing investment decisions in a competitive and uncertain electricity market.

EPFL2007

Courtesy As a Means to Coordinate

Panayiotis Danassis, Boi Faltings

We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to efficient and fair allocations based on a simple convention of courtesy. We prove that following such convention induces a strategy which constitutes an epsilon-subgame-perfect equilibrium of the repeated allocation game with discounting. Simulation results highlight the effectiveness of CA^3NONY as compared to state-of-the-art bandit algorithms, since it achieves more than two orders of magnitude faster convergence, higher efficiency, fairness, and average payoff.

International Foundation for Autonomous Agents and Multiagent Systems2019

Multi-agent modelling of electricity markets

Abdelaziz Lahlou

EPFL2007

Multi-agent modelling of electricity markets

Multi-agent Coordination using Local Search

Courtesy As a Means to Coordinate

Multi-agent modelling of electricity markets

Multi-agent Coordination using Local Search

Courtesy As a Means to Coordinate