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There are few systematic methodologies capable of predicting and leveraging the reserve capacity potential of large populations of thermostatically-controlled loads (TCLs). For such reserves to have economic and technical value, it is essential that demand-side flexibility aggregators and system operators be able to do so quickly and robustly. In addition, aggregators need to ensure TCL users' comfort and end use satisfaction. Part I of this two-part paper introduces an analytical approach to characterize and control the statistical bounds on the potential aggregated response of a population of TCLs, while ensuring users' comfort satisfaction. First, the uncertainty associated with the instantaneous power consumption of a typical TCL is described by a set of random variables and their statistics. TCL statistics are then employed to characterize the exploitable flexibility from a large population of similar devices. From this, a control strategy and parameters are introduced for sporadic (i.e., contingency-type) reserve provision by the population. In Part II, the proposed analytical approach and control strategy are validated for the special case of electric water heaters (EWHs). Further, the trade-off between demand response capacity and EWH users' comfort satisfaction is investigated through several case studies. [ABSTRACT FROM AUTHOR]

This paper proposes a security-constrained forward market clearing algorithm within which the inherent characteristics of demand flexibility are acknowledged when the provision of reserve from the demand side is considered. In the proposed formulation, we co-optimize the cost of scheduling the appropriate resources to guarantee the security of the system and the expected cost of operating under any credible system state. In addition, we consider that consumers can offer to provide spinning reserve capacity deployable by voluntary load reductions in response to contingencies. Due to the load recovery effect (i.e., since energy usage is essentially postponed when demand-side reserve is deployed), in post-contingency states, any voluntary reduction in the load has to be accompanied by a subsequent increase in demand from the initial forecast. We demonstrate that the marginal value of the reserve provided by the demand can only be calculated taking into account the cost of supplying the recovery consumption. Flexible consumers can reduce their payments on average if energy is settled through real-time prices. [ABSTRACT FROM PUBLISHER]

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns. [ABSTRACT FROM AUTHOR]