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This paper introduces a module-based approach to modelling and controlling electricity infrastructure with a high penetration of distributed renewable energy resources. Each energy-converting component is defined as a module and is represented in terms of its local variables and the interaction variables between the module and the transmission network. The structure of the electricity infrastructure is preserved in the proposed dynamical model. Therefore, it is possible to specify the performance subobjectives of each module for a given range of variations in interaction variables and to ensure that the local subobjectives are met through multidirectional distributed sensing and actuation. This approach provides a systematic means of analysing the infrastructure dynamics with increasing penetration of distributed renewable energy resources such as wind and solar. Sufficient conditions on modules and network specifications are derived under which the system-wide dynamics are stabilised. Based on these conditions, an interactive communication protocol between the modules and system operator could be implemented for distributed stabilisation. An IEEE 14 bus system is used to illustrate the concepts put forward.

In this paper an agent-based approach characterizing auction-style electricity spot market dynamics is proposed. This method captures the learning and decisionmaking of rational agents which cannot be modeled using a supply-demand equilibrium approach. Because the auctions are repeated, an agent improves its decisions through time or through adaptive learning. Furthermore, instead of elaborately searching for an optimal bidding strategy for each agent, some sensible and practical bidding strategies are assigned to the agents. This strategic behavior depends highly on information available to the agents before a bidding decision is made. The model mainly focuses on understanding the role of learning by the generators and, accordingly, on their strategic bidding behavior in the market.

This paper analyses long term price trends in deregulated electricity markets. A price model using explicit supply and demand states is introduced. A stochastic model for demand growth drives future uncertainty, and investors respond by adding new capacity based on price feedback. The effects on price dynamics of delays in the supply response, through information lag or construction time, are illustrated through simulations. The model is extended to account for the physical reliability problem resulting from a lack of generation capacity. It is shown how inappropriate intervention by regulators, through the use of price caps, can result in a critical decrease in the markets’ reserve margin.

This paper considers two methods for transmission service to the electric grid users in a market structure with both long-term bilateral agreements and short-term spot market. The first type of service is the one in which a transmission system provider is the main decision maker solving optimization at the system-wide level. The second type of service allows for the system users to indirectly control flows on the grid by deciding how much demand to exert for system use. Both formulations fall under the category of optimal transmission resource allocation problems. The formulations in this paper require solutions to a stochastic dynamic programming problem. The paper emphasizes process characterization and the assessment of the computational tools for solving the underlying stochastic optimization problems.