ElsevierEscrivá-Escrivá, G.; Segura Heras, I.; Alcázar-Ortega, M. (2010). Application of an energy management and control system to assess the potential of different control strategies in HVAC systems. Energy and Buildings. 42(11):2258-2267. doi:10.1016/j.enbuild.2010 AbstractThe significant and continuous increment in the global electricity consumption is asking for energy saving strategies. Efficient control for heating, ventilation and air-conditioning systems (HVAC) is the most cost-effective way to minimize the use of energy in buildings. In this framework, an energy management and control system (EMCS) has been developed to schedule electricity end-uses in the campus of the Universidad Politécnica de Valencia (UPV), Spain. This paper presents an evaluation performed by using the EMCS of different control strategies for HVAC split systems. It is analyzed the effect of different schedules for a common air-conditioning device and demand response strategies are tested in several situations. The economic saving is calculated taking into account the electricity contract clauses.Finally, a test is made for the control of a group of similar devices in order to reduce the maximum peak power in consumption and to obtain a flexible load shape with the HVAC loads.The studies are then extrapolated to a larger system, the whole University campus, for which energy and economic savings are quantified.
Due to the current high energy prices it is essential to find ways to take advantage of new energy resources and enable consumers to better understand their load curve. This understanding will help improve customer flexibility and their ability to respond to price or other signals from the electricity market. In this scenario, one of the most important steps is to carry out an accurate calculation of the expected consumption curve, i.e. the baseline. Subsequently, with a proper baseline, customers can participate in demand response programs and verify performed actions. This paper presents an artificial neural network (ANN) method for short-term prediction of total power consumption in buildings with several independent processes. This problem has been widely discussed in recent literature but a new point of view is proposed. The method is based on two fundamental features: total consumption forecast based on independent processes of the considered load or end-uses; and an adequate selection of the training data set in order to simplify the ANN architecture. Validation of the method has been performed with the prediction of the whole consumption expressed as 96 active energy quarterhourly values of the Universitat Politècnica de València, a commercial customer consuming 11,500 kW.
The authors present a methodology, which is demonstrated with some applications to the commercial sector, in order to validate a Demand Response (DR) evaluation method previously developed and applied to a wide range of industrial and commercial segments, whose flexibility was evaluated by modeling. DR is playing a more and more important role in the framework of electricity systems management for the effective integration of other Distributed Energy Resources. Consequently, customers must identify what they are using the energy for in order to use their flexible loads for management purposes. Modeling tools are used to predict the impact of flexibility on the behavior of customers, but this result needs to be validated since both customers and grid operators have to be confident in these flexibility predictions. An easy-touse two-steps method to achieve this goal is presented in this paper.
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