Refrigeration Systems with Thermal Energy Storage

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Refrigeration Systems with Thermal Energy Storage ( refrigeration-systems-with-thermal-energy-storage )

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Mathematics 2022, 10, 3167 14 of 27 Q ̇ TES,sec, and TSH). The decentralised strategy presented in [25] is applied, thus only a few details will be remarked below. Firstly, a cascade strategy is applied to manipulate the expansion valves, in such a way that the refrigerant mass flows m ̇ e and m ̇ TES are used as virtual manipulated variables. The same strategy would be applied if considering the TES pump power/speed as the actual manipulated variable, in such a way that the secondary mass flow that circulates through the TES tank m ̇ TES,sec would be considered as a virtual manipulated variable. Secondly, a supervisory strategy is applied for the degree of superheating TSH, when applicable (modes 1–3), to ensure that it never decreases under a certain security limit. Regarding energyefficiencyissues,thesetpointTref iscomputedfollowingthestrategyappliedtothe SH original refrigeration facility described in [30], where the compressor speed N is forced to be as low as possible while holding TSH over a certain security limit. Finally, a decoupling strategy is applied to the control of the most coupled cooling powers, i.e., Q ̇ e,sec and Q ̇ TES in mode 1. 4.3. Scheduling Strategy As stated in Section 4.1, the scheduling problem is posed as a non-linear optimization, whose main features are detailed below. • Decision variables: The decision variables are the references of the three relevant cooling powers along the prediction horizon PH: {Q ̇ re f (t − 1 + k), Q ̇ re f (t − 1 + k), e,sec TES Q ̇ref (t−1+k)} ∀k∈[1,PH],onceanoperatingmodeschedulingisproposed TES,sec based on the cooling demand profile and the energy prices along the day. • Prediction model: The simplified dynamic model proposed in Section 3.1 is applied to obtain predictions on the evolution of the TES tank state vector xTES along the prediction horizon, as shown in Equation (17): xTES(t+k)=g(xTES(t−1+k),Q ̇ref (t−1+k),Q ̇ref (t−1+k),Tsurr(t−1+k)) ∀k∈[1,PH]. (17) TES TES,sec • Constraints: – Cooling demand: An overall power forecast is known, Q ̇ re f (t − 1 + k) ∀k ∈ sec [1, PH]. Therefore, the constraint described in Equation (18) must be imposed along the prediction horizon: Q ̇ref (t−1+k)+Q ̇ref (t−1+k) = Q ̇ref(t−1+k) ∀k ∈ [1,PH]. (18) e,sec TES,sec sec – Power feasibility: The maximum and minimum values of all cooling powers must be imposed along the prediction horizon, according to the selected operating mode, resulting in the constraints shown in Equation (19): Q ̇ref (t−1+k)∈[Q ̇ref,min(t−1+k), Q ̇ref,max(t−1+k)], e,sec Q ̇ref e,sec (t−1+k) ∈ [Q ̇ref,min(t−1+k), e,sec Q ̇ref,max(t−1+k)], TES Q ̇ref TES,sec Notice that, when a certain cooling power is forced to be zero at a given operating mode, the minimum and maximum values for the corresponding cooling power are also zero in Equation (19). The maximum and minimum values of the cooling powers are also shown to depend in some cases on the TES tank state vector [25]; thus, they may vary along the prediction horizon due to the evolution of xTES. TES (t−1+k) ∈ [Q ̇ref,min(t−1+k), TES Q ̇ref,max(t−1+k)], (19) TES,sec TES,sec ∀k ∈ [1, PH].

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