Wesley Cole

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.

Building energy model reduction for model predictive control using OpenStudio

Model-based predictive control for buildings is an active area of research. Significant effort has been placed on developing accurate and computationally efficient reduced-order models that can be implemented in predictive controllers. During a buildings design and construction process, detailed building models are often created by experienced building modelers. These models are often too complex to be directly implemented in control applications. Reducing these models to low-order models can be time-consuming and require additional skills beyond those possessed by building modelers. In this paper we demonstrate simple reduction of building models using the OpenStudio analysis framework in a script-based environment. OpenStudio is a cross-platform tool for modeling and analysis of building energy systems. A reduced-order model is created for a simple building and an economic-based model predictive controller is used to minimize summertime cooling costs in an electricity market with real-time pricing.

Use of model predictive control to enhance the flexibility of thermal energy storage cooling systems

This paper investigates the application of a model predictive controller (MPC) to both a traditional and a novel chilled water thermal energy storage system over for an Austin, Texas, climate. In the novel system, the thermal storage discharges during peak electricity times to meet building cooling load and to supply reduced temperature water for heat rejection in the chillers condenser. Chiller efficiency improves as the condenser water temperature decreases, shifting more electrical usage to off-peak hours, but may increase overall electrical usage. The MPC is designed to optimize the discharge and recharge of the thermal storage in order to minimize operation costs or energy consumption over a 24-hour prediction horizon. The ability of MPC to level the electrical load profile is also considered. The way in which demand charges are considered in the objective function can greatly influence the systems electrical load profile.

2015 Standard Scenarios Annual Report: U.S. Electric Sector Scenario Exploration

This report is one of several products resulting from an initial effort to provide a consistent set of technology cost and performance data and to define a conceptual and consistent scenario framework that can be used in the National Renewable Energy Laboratory’s (NREL’s) future analyses. The long-term objective of this effort is to identify a range of possible futures of the U.S. electricity sector in which to consider specific energy system issues through (1) defining a set of prospective scenarios that bound ranges of key technology, market, and policy assumptions and (2) assessing these scenarios in NREL’s market models to understand the range of resulting outcomes, including energy technology deployment and production, energy prices, and carbon dioxide (CO2) emissions.

Utility-scale lithium-ion storage cost projections for use in capacity expansion models

This work presents U.S. utility-scale battery storage cost projections for use in capacity expansion models. We create battery cost projections based on a survey of literature cost projections of battery packs and balance of system costs, with a focus on lithium-ion batteries. Low, mid, and high cost trajectories are created for the overnight capital costs and the operating and maintenance costs. We then demonstrate the impact of these cost projections in the Regional Energy Deployment System (ReEDS) capacity expansion model. We find that under reference scenario conditions, lower battery costs can lead to increased penetration of variable renewable energy, with solar photovoltaics (PV) seeing the largest increase. We also find that additional storage can reduce renewable energy curtailment, although that comes at the expense of additional storage losses.

Evaluating the value of high spatial resolution in national capacity expansion models using ReEDS

Power sector capacity expansion models (CEMs) have a broad range of spatial resolutions. This paper uses the Regional Energy Deployment System (ReEDS) model, a longterm national scale electric sector CEM, to evaluate the value of high spatial resolution for CEMs. ReEDS models the United States with 134 load balancing areas (BAs) and captures the variability in existing generation parameters, future technology costs, performance, and resource availability using very high spatial resolution data, especially for wind and solar modeled at 356 resource regions. In this paper we perform planning studies at three different spatial resolutions-native resolution (134 BAs), state-level, and NERC region level-and evaluate how results change under different levels of spatial aggregation in terms of renewable capacity deployment and location, associated transmission builds, and system costs. The results are used to ascertain the value of high geographically resolved models in terms of their impact on relative competitiveness among renewable energy resources.

Addressing the Peak Power Problem Through Thermal Energy Storage

In the United States, the electrical power grid is divided into three primary regions: the Western Interconnection, the Eastern Interconnection, and the Texas Interconnection. Each of these regions struggles with peak power issues, but this case study will focus on the Texas Interconnection, which is operated by the Electricity Reliability Council of Texas (ERCOT).This chapter discusses the opportunity to shift one of the largest electricity loads (air-conditioning) from the expensive aftrenoon peak to the cheaper nighttime hours using Thermal Energy Storage (TES), which is used for storing “cooling” in the form of chilled wate, and outlines a model for finding an optimal design for it.

Community-scale air conditioning control for high penetration of rooftop photovoltaics

This paper investigates the potential of coordinated air conditioning control for a simulated community of 900 homes with a high penetration of rooftop solar photovoltaic (PV) panels. The simulated community of homes is created from an extensive data set including home energy audits, homeowner surveys, and electricity meter measurements from actual homes in Austin, Texas, USA. Coordinated air conditioning control in the homes is simulated using a rolling horizon model predictive controller to minimize the peak demand of the community using both centralized and decentralized control methods. By manipulating thermostat set points, the controller takes advantage of the thermal mass of the buildings to store thermal energy. In all cases considered, the centralized controller outperforms the decentralized controller, but both lead to significant reductions in peak electricity demand. We find that decentralized control achieves nearly the same peak reduction as the centralized control method when all homes have rooftop PV. We also find that coordinated air conditioning control achieves a marginally smaller benefit as the penetration of rooftop PV increases.