Model Predictive Control for Central Plant Optimization with Thermal Energy Storage

An optimization framework is used in order to determine how to distribute both hot and cold water loads across a central energy plant including heat pump chillers, conventional chillers, water heaters, and hot and cold water (thermal energy) storage. The objective of the optimization framework is to minimize cost in response to both real-time energy prices and demand charges. The linear programming framework used allows for the optimal solution to be found in real-time. Real-time optimization lead to two separate applications: A planning tool and a real-time optimization tool. In the planning tool the optimization is performed repeatedly with a sliding horizon accepting a subset of the optimized distribution trajectory horizon as each subsequent optimization problem is solved. This is the same strategy as model predictive control except that in the design and planning tool the optimization is working on a given set of loads, weather (e.g. TMY data), and real-time pricing data and does not need to predict these values. By choosing the varying lengths of the horizon (2 to 10 days) and size of the accepted subset (1 to 24 hours), the design and planning tool can be used to find the design year’s optimal distribution trajectory in less than 5 minutes for interactive plant design, or the design and planning tool can perform a high fidelity run in a few hours. The fast solution times also allow for the optimization framework to be used in real-time to optimize the load distribution of an operational central plant using a desktop computer or microcontroller in an onsite Enterprise controller. In the real-time optimization tool Model Predictive Control is used; estimation, prediction, and optimization are performed to find the optimal distribution of loads for duration of the horizon in the presence of disturbances. The first distribution trajectory in the horizon is then applied to the central energy plant and the estimation, prediction, and optimization is repeated in 15 minutes using new plant telemetry and forecasts. Prediction is performed using a deterministic plus stochastic model where the deterministic portion of the model is a simplified system representing the load of all buildings connected to the central energy plant and the stochastic model is used to respond to disturbances in the load. The deterministic system uses forecasted weather, time of day, and day type in order to determine a predicted load. The estimator uses past data to determine the current state of the stochastic model; the current state is then projected forward and added to the deterministic system’s projection. In simulation, the system has demonstrated more than 10% savings over other schedule based control trajectories even when the subplants are assumed to be running optimally in both cases (i.e., optimal chiller staging, etc.). For large plants this can mean savings of more than US $1 million per year

Autonomous Optimization and Control for Central Plants with Energy Storage

A model predictive control (MPC) framework is used to determine how to optimize the distribution of energy resources across a central energy facility including chillers, water heaters, and thermal energy storage; present the results to an operator; and execute the plan. The objective of this MPC framework is to minimize cost in real-time in response to both real-time energy prices and demand charges as well as allow the operator to appropriately interact with the system. Operators must be given the correct intersection points in order to build trust before they are willing to turn the tool over and put it into fully autonomous mode. Once in autonomous mode, operators need to be able to intervene and impute their knowledge of the facilities they are serving into the system without disengaging optimization. For example, an operator may be working on a central energy facility that serves a college campus on Friday night before a home football game. The optimization system is predicting the electrical load, but does not have knowledge of the football game. Rather than try to include every possible factor into the prediction of the loads, a daunting task, the optimization system empowers the operator to make human-in-the-loop decisions in these rare scenarios without exiting autonomous (auto) mode. Without this empowerment, the operator either takes the system out of auto mode or allows the system to make poor decisions. Both scenarios will result in an optimization system that has low “on time†and thus saves little money. A cascaded, model predictive control framework lends itself well to allowing an operator to intervene. The system presented is a four tiered approach to central plant optimization. The first tier is the prediction of the energy loads of the campus; i.e., the inputs to the optimization system. The predictions are made for a week in advance, giving the operator ample time to react to predictions they do not agree with and override the predictions if they feel it necessary. The predictions are inputs to the subplant-level optimization. The subplant-level optimization determines the optimal distribution of energy across major equipment classes (subplants and storage) for the prediction horizon and sends the current distribution to the equipment level optimization. The operators are able to use the subplant-level optimization for “advisory†only and enter their own load distribution into the equipment level optimization. This could be done if they feel that they need to be conservative with the charge of the tank. Finally, the equipment level optimization determines the devices to turn on and their setpoints in each subplant and sends those setpoints to the building automation system. These decisions can be overridden, but should be extremely rare as the system takes device availability, accumulated runtime, etc. as inputs. Building an optimization system that empowers the operator ensures that the campus owner realizes the full potential of his investment. Optimal plant control has shown over 10% savings, for large plants this can translate to savings of more than US $1 million per year

An iterative scheme to hierarchically structured optimal energy management of a microgrid

In this paper we address the optimal energy management of a microgrid composed of multiple sub-units, each one including one or more buildings sharing some common resources. The goal of the microgrid operator is to match a given electrical energy profile agreed with the operator of the main grid. We propose a decentralized solution scheme based on a hierarchical structure involving three layers: single building, sub-unit, and microgrid operator. At the level of each building, thermal and electrical energy requests are minimized while guaranteeing a certain comfort and quality of service to the building occupants. Optimization of the use of common resources (storages and technological devices) is performed by each sub-unit based on the energy requests of the buildings composing the sub-unit and the cost signal received by the microgrid operator. Each sub-unit minimizes its electrical energy cost as computed based on its own cost signal, while the microgrid operator updates all cost signals based on the outcome of the decentralized optimization, so as to coordinate the sub-units and match the given reference profile. A numerical example shows the efficacy of the approach

A compositional framework for energy management of a smart grid: A scalable stochastic hybrid model for cooling of a district network

The goal of this paper is to introduce a compositional modeling framework for the energy management of a smart grid that operates connected to the main grid. The focus is on the cooling of a district network composed of multiple buildings that possibly share resources such as storages, chillers, combined heat and power units, and renewable power generators. We adopt a modular perspective where components are described in terms of energy fluxes and interact by exchanging energy. Model dimension and complexity depend on the number and type of components that are present in the specific configuration. Energy management problems like the minimization of the electrical energy cost or the tracking of some electrical energy profile can be addressed in the proposed framework via different control strategies and architectures

Optimal energy management of a building cooling system with thermal storage: A convex formulation

This paper addresses the optimal energy management of a cooling system, which comprises a building composed of a number of thermally conditioned zones, a chiller plant that converts the electrical energy in cooling energy, and a thermal storage unit. The electrical energy price is time-varying, and the goal is to minimize the electrical energy cost along some look-ahead time horizon while guaranteeing an appropriate level of comfort in the building. A key feature of the approach is that the temperatures in the zones are treated as control inputs together with the cooling energy exchange with the storage. This simplifies the enforcement of comfort, which can be directly imposed through appropriate constraints on the control inputs. Furthermore, a model that is easily scalable in the number of zones and convex as a function of the control inputs is derived based on energy balance equations. A convex constrained optimization program is then formulated to address the optimal energy management with reference to the forecasted operating conditions of the building. Simulation results show the efficacy of the proposed approach

Optimal scheduling of chiller plant with thermal energy storage using mixed integer linear programming

In this paper, we consider the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm. © 2013 AACC American Automatic Control Council

Optimal scheduling of chiller plant with thermal energy storage using mixed integer linear programming

In this paper, we consider the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm. © 2013 AACC American Automatic Control Council

Development of a Model and Optimal Control Strategy for the Cal Poly Central Plant and Thermal Energy Storage System

This thesis develops a calibrated model of the Cal Poly Central Chilled Water Plant with Thermal Energy Storage for use in determining an optimal operating control strategy. The model was developed using a transient systems simulation program (TRNSYS) that includes plant performance and manufacturer data for the primary system components, which are comprised of pumps, chillers, cooling towers, and a thermal energy storage tank. The model is calibrated to the actual measured performance of the plant using the current control strategy as a baseline. By observing and quantifying areas for potential improvement in plant performance under conditions of high campus cooling load demands, alternative control strategies for the plant are proposed. Operation of the plant under each of these control strategies is simulated in the model and evaluated for overall energy and demand-usage cost savings. These results are used to recommend improvements in the plant’s current control strategy, as well as to propose an optimal control strategy that may be applied to reduce plant operating costs. The results of the model identify that the plant can perform more economically by employing more chiller power to charge the Thermal Energy Storage tank to higher capacities during overnight periods when the utility rates are lower. Staging the operation of the different chillers to more precisely follow the tank charges during these off-peak periods can ensure faster tank charging when its capacity may not be sufficient to meet the peak and part-peak cooling load demands. A proposed control strategy to accomplish this breaks the overnight Off-Peak rate period into three periods with separate control setpoints, which are designed to maintain the tank charge capacity at the minimum levels to be able to accommodate the daily campus cooling demands during peak and part-peak hours