This thesis is interested in improving the operation of a parabolic trough technology based solar thermal power plant by means of automatic control. One of the challenging issues in a solar thermal power plant, from the control point of view, is to maintain the thermal process variables close to their desired levels. In contrast to a conventional power plant where fuel is used as the manipulated variable, in a solar thermal power plant, solar radiation cannot be manipulated and in fact it ironically acts as a disturbance due to its change on a daily and seasonal basis.
The research facility ACUREX is used as a test bed in this thesis. ACUREX is a typical parabolic trough technology based solar thermal power plant and belongs to the largest research centre in Europe for concentrating solar technologies, namely the Plataforma Solar de Almería (PSA) in south-east Spain. The plant exhibits nonlinearities as well as resonance characteristics that lie well within the desired control bandwidth. Failure to adequately capture the resonance characteristics of the plant results in an undesired oscillatory control performance. Moreover, measured disturbances are an integral part of the plant and while some of the measured disturbances do not have a significant impact on the operation of the plant, others do.
Hence, with the aim of handling the plant nonlinearities and capturing the plant resonance characteristics, while taking explicit account of the measured disturbances, in this thesis a gain scheduling feedforward predictive control strategy is proposed. The control strategy is based upon a family of local linear time-invariant state space models that are estimated around a number of operating points. The locally estimated linear time-invariant state space models have the key novelty of being able to capture the resonance characteristics of the plant with the minimal number of states and hence, simple analysis and control design.
Moreover, while simple classical, series and parallel, feedforward configurations have been proposed and used extensively in the literature to mitigate the impact of the measured disturbances of the ACUREX plant, the proposed control strategy incorporates a feedforward systematically by including the effects of the measured disturbances of the ACUREX plant into the predictions of future outputs.
In addition, a target (set point) for a control strategy is normally set at the ACUREX plant by the plant operator. However, in this thesis it is argued that, in parallel, the operator must choose between potentially ambitious and perhaps unreachable targets and safer targets. Ambitious targets can lead to actuator saturation and safer targets imply electricity production losses.
Hence, in this thesis a novel two-layer hierarchical control structure is proposed with the gain scheduling feedforward predictive control strategy being deployed in a lower layer and an adequate reachable reference temperature being generated from an upper layer. The generated reference temperature drives the plant near optimal operating conditions, while satisfying the plant safety constraints, without any help from the plant operator and without adding cost.
The proposed two-layer hierarchical control strategy has the potential benefits of: (i) maximising electricity production; (ii) reducing the risk of actuator saturation; (iii) extending the life span of various elements of the plant (e.g. synthetic oil, pump and valves) and (iv) limiting the role of the plant operator.
The efficacy of the proposed two-layer hierarchical control strategy is evaluated using a nonlinear simulation model that approximates the dynamic behaviour of the ACUREX plant. The nonlinear simulation model is constructed in this thesis and validated in the time and frequency domain.
