Many problems have arose along with the practical implementation of restructured electrical business in USA and European Union such as lack of generation, network constraints, etc. A good example for these problems is the scarce participation of the demand in the electricity markets-energy, reserve and other ancillary services-problem that can be solved through new Demand Responsive Programs, aimed to replace the traditional Demand Side Management programs into voluntary demand participation programs. A methodology for the generation of demand side bids and offers in large customer facilities and a real application to a university customer is presented in this paper. This methodology is based on knowledge of the physical processes involved in the electricity consumption and the flexibility in the required supply. The result of the methodology proposed is a set of Demand Packages, that can be used to participate in different electricity markets, whose possibilities-in the market arena-will be explored in a consequent paper.

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The problem of load modeling for Demand Side Management ( E M ) purposes is addressed in this paper. The proposed load models rely on information about both the physical characteristics of elemental load devices at the distribution level, and usage statistics of these devices.Although the class of models discussed here has been previously proposed in the literature, its suitability for DSM purposes is definitely established by showing how the models can be a tool for real E M actions evaluation. Some results are shown. -INTBODUCTIONThe use of Demand-Side Managementalternatives is gaining adepts between utilities and distribution companies in order to achieve a better operation of the Electric Power System.Two different approaches may be used to cope with the growth of the demand in an Electric Power System. The first one is to expand the Power System so that the new energy requirements can be met (Supply-Side policy). The second one is to try to influence the electric energy consumption so as to reduce the investment requirements (Demand-Side policies). Demand Side Managementhas been defined as those activities oriented to influence customer uses of electricity in ways that will produce the desired changes in the load shape [ l l . We will refer t o the Control actions directly performed upon the customer loads as Load Management (LM) actions.The reason for considering the possibility of influencing the customer uses must be found in the continuous rise in the cost of electricity and equipment, the availability of the required technology, more severe environmental constraints on power system generat ion, transmission and expansion, and the necessity to offer new options to the customer. Voltage reduction is a typical LM action that has been traditionally used by the utility for power peak consumption reduction.Some other actions need to be considered as potential LM control actions, mainly those related to the possibility of end-user load shedding: load interruption and load cycling.Obviously, the possibility of performing these kinds of actions upon the consumers must be attached to a flexible rates policy.One of the most critical problems when considering the application of DSM by the utility is to be able to assess whether this policy is going to produce the desired effects or not. Thus, in order to evaluate the DSM policies, it is necessary to have load models that can fulfill at least two objectives: First they should provide the necessary information to evaluate the benefits obtained through the use of the DSM and, secondly, they must allow the evaluation of every control action from the end-customer side, for example, through the evaluation of some "comfort index".These comfort indices, in conjunction with a proper rates structure, can become very important in securing U high level of acceptance of DSM policies among the customers.The load models we are about to discuss in this paper have appeared earlier elsewhere in the literature [61, [71, [81, [91 and [121. However, due to their relative math...

Load forecasting models are of great importance in Electricity Markets and a wide range of techniques have been developed according to the objective being pursued. The increase of smart meters in different sectors (residential, commercial, universities, etc.) allows accessing the electricity consumption nearly in real time and provides those customers with large datasets that contain valuable information. In this context, supervised machine learning methods play an essential role. The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting. To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain). In addition to temperature, calendar variables as well as different types of special days are considered as predictors to improve the predictions. Finally, a real application to the Spanish Electricity Market is developed: 48-h-ahead predictions are used to evaluate the economical savings that the consumer (the campus university) can obtain through the participation as a direct market consumer instead of purchasing the electricity from a retailer.

Diesel-electric

Several measures of volatility have been developed in order to quantify the degree of uncertainty of an energy price series, which include historical volatility and price velocities, among others. This paper suggests using the permutation entropy, topological entropy and the modified permutation entropy as alternatives to measure volatility in energy markets. Simulated data show that these measures are more appropriate to quantify the uncertainty associated to a time series than those based on the standard deviation or other measures of dispersion. Finally, the proposed method is applied to some typical electricity markets: Nord Pool, Ontario, Omel and four Australian markets.