Kody M. Powell

Optimization and advanced control of thermal energy storage systems

Abstract This paper reviews the optimization and control of thermal energy storage systems. Emphasis is given to thermal storage applied to combined heat and power systems, building systems, and solar thermal power systems. The paper also discusses how applications of thermal storage can benefit the chemical industry. Optimization of the design and control of thermal storage systems improves plant performance and improves the management of transient energy loads in a variety of applications. In order to maximize the benefits of thermal storage, it is necessary to include advanced multivariate constrained controls, such as model predictive control. Thermal storage also increases system flexibility, allowing the incorporation of intermittent renewable energy sources. The flexibility of thermal storage will play an increasingly important role as utilities implement smart grid technology with time-of-use electricity pricing. Lastly, thermal energy storage improves system economics by reducing required equipment sizes, improving efficiency, and reducing equipment wear.

Dynamic optimization of a solar thermal energy storage system over a 24 hour period using weather forecasts

A solar thermal power plant is used as a case study for dynamic heat integration with thermal energy storage. Findings show that thermal energy storage gives the system the ability to make the power dispatchable. Additionally, by solving a 24-hour dynamic optimization problem where the plant temperatures and power output are variable allows the system to capture and harvest a higher percentage of solar energy, with the most benefit occurring on mostly cloudy days. The solar energy captured increases 64% from 4.75 MWh to 7.80 MWh using this scheme. Hybrid plant operation and the ability to bypass the storage tanks further improve the system performance.

Control of a large scale solar thermal energy storage system

Dynamic simulation results for a thermal energy storage (TES) unit used in a parabolic trough concentrated solar power (CSP) system are presented. A two-tank-direct method is used for the thermal energy storage. The heat transfer fluid flow rate through the solar collector maintains a constant outlet temperature and the flow rate through the boiler regulates power output. The use of storage greatly improves the systems ability to provide power at a constant rate despite significant disturbances in the amount of solar radiation available. It can also shift times of power generation to better match times of consumer demand. By contrast, a CSP system without storage undergoes large fluctuations in power output, particularly during intermittent cloud cover. Adding a storage system increases the solar share of the power plant by over 80%, reducing the requirement for supplementary fossil energy by as much as 8.4 MWh daily.

Nonlinear model predictive control for a heavy-duty gas turbine power plant

A nonlinear model predictive control (NMPC) scheme is applied to a 166 MW a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads. The NMPC solution is obtained by applying orthogonal collocation on finite elements to convert the dynamic problem to a set of algebraic equations, which can be solved as a nonlinear programming (NLP) problem. This NLP problem is then solved using analytical derivatives and an interior-point algorithm to ensure fast solution times. The computation time at each control step was sufficiently faster than the sampling rate (1 sec), allowing real-time implementation of NMPC for the gas turbine power plant.

Dynamic Real-Time Optimization of Air-Conditioning Systems in Residential Houses with a Battery Energy Storage under Different Electricity Pricing Structures

Abstract This paper investigates potential cost savings in operating residential houses air-conditioning systems through dynamic real-time optimization (D-RTO). Standard design data collected from BEopt (Building Energy Optimization) software were used in Matlab/Simulink to simulate cooling energy consumption of a house model in Salt Lake City, Utah. Two different electricity pricing structures were employed; time-of-use (TOU) and real-time pricing (RTP). The D-RTO was formulated as a linear programming problem with the air conditioning temperature setpoint, the cooling energy, the battery state of charge, the charging energy and discharging energy of the battery being decision variables of the model. The D-RTO determines day-ahead values for the dynamic variables of the system based on the weather forecast and energy price signals. The D-RTO uses a model predictive control (MPC) like approach in which the problem is solved for 24 hours in advance, but the solutions are implemented on a receding horizon, where the prediction interval moves forward by one hour each time step. Results show significant energy cost reductions for the optimized cases with a battery energy storage versus the cases without a battery under each pricing structure. Also, both pricing structures are ranked based on their capabilities for cost and energy savings and peak shifting.