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Heating, ventilation and air conditioning for residential and commercial buildings requires a substantial share of electric energy, and ultimately drives summer peak demand in the United States. Variable electric rates are becoming more common in the residential market, as utilities try to encourage users to shift their energy demand. Model predictive controls, one method of reducing energy usage, employ an optimization model to minimize peak demand, energy usage, or electricity costs. This paper details the development of a co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls. The framework integrates commercially available software to: (i) simulate all energy interactions between the building, internal gains, outdoor environment, and heating and cooling systems via a building energy simulation program (EnergyPlus), (ii) algebraically formulate an optimization problem (with AMPL) using a black-box, reduced-order model for rapid calculations, (iii) employ Simulink as the environment that links calls to EnergyPlus and AMPL, and (iv) solve the optimization model (with CPLEX) to minimize electricity costs and user discomfort. Variable electric time-of-use rates are analyzed in the context of total cooling electricity costs, thermal comfort of users, and peak demand shedding. The framework uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule.

A mathematical model designs and operates a hybrid power system consisting of diesel generators, photovoltaic cells and battery storage to minimize fuel use at remote sites subject to meeting variable demand profiles, given the following constraints: power generated must meet demand in every time period; power generated by any technology cannot exceed its maximum rating; and best practices should be enforced to prolong the life of the technologies. We solve this optimization model in two phases: (i) we obtain the design and dispatch strategy for an hourly load profile, and (ii) we use the design strategy, derived in (i), as input to produce the optimal dispatch strategy at the minute level. Our contributions consist of: combining a year-long hourly optimization procurement strategy with a minute-level dispatch strategy, and using a high-fidelity battery model at the minute-level derived from electrochemical engineering principles that incorporate temperature and voltage transient effects. We solve both phases of the optimization problem to within 5% of optimality and demonstrate that solutions from the minute-level model more closely match the load, more closely capture battery and generator behavior, and provide fuel savings from a few percent to 30% over that provided by the hour-level model for the tested scenarios.

Mining is one of the most energy-intensive industries worldwide. It also provides a critical source of raw materials for the manufacturing, transportation, construction, and energy sectors. Demand for raw materials is projected to increase as the world population grows and many low-income economies become middle-income countries. This growth in mineral demand, coupled with falling mineral ore grade, will likely increase the mining industry’s energy demand, used for activities across exploration, extraction, beneficiation and processing, and refining. At the time of this writing, mine operations are – due to their remoteness – dependent on fossil fuels such as diesel, heavy oils, and coal. In principle, mining could use energy recovery, renewable energy, and carbon capture to supplement, replace, or mitigate the impacts of fossil fuel use. However, a combination of renewable-energy technologies would be required. We explore challenges, opportunities, and enabling approaches to integrate renewable energy technologies into mining operations by examining the literature, including academic work, technical reports, and data produced by international agencies. We find that despite numerous opportunities, technical issues still need to be considered, but solutions can tailor renewables to the mining industry. Further research should focus on identifying specific opportunities, technologies, and implementation strategies across the value chain of a variety of minerals with similar operational procedures.

Concentrating solar power towers, which include a steam-Rankine cycle with molten salt thermal energy storage, is an emerging technology whose maximum effectiveness relies on an optimal operational and dispatch policy. Given parameters such as start-up and shut-down penalties, expected electricity price profiles, solar availability, and system interoperability requirements, this paper seeks a profit-maximizing solution that determines start-up and shut-down times for the power cycle and solar receiver, and the times at which to dispatch stored and instantaneous quantities of energy over a 48-h horizon at hourly fidelity. The mixed-integer linear program (MIP) is subject to constraints including: (i) minimum and maximum rates of start-up and shut-down, (ii) energy balance, including energetic state of the system as a whole and its components, (iii) logical rules governing the operational modes of the power cycle and solar receiver, and (iv) operational consistency between time periods. The novelty in this work lies in the successful integration of a dispatch optimization model into a detailed techno-economic analysis tool, specifically, the National Renewable Energy Laboratory’s System Advisor Model (SAM). The MIP produces an optimized operating strategy, historically determined via a heuristic. Using several market electricity pricing profiles, we present comparative results for a system with and without dispatch optimization, indicating that dispatch optimization can improve plant profitability by 5–20% and thereby alter the economics of concentrating solar power technology. While we examine a molten salt power tower system, this analysis is equally applicable to the more mature concentrating solar parabolic trough system with thermal energy storage.

As part of an energy-reduction study at remote sites, we explore a power system comprised of hybrid renewable energy technologies, specifically, photovoltaic cells, battery storage, and diesel generators. An optimization model determines the design and dispatch strategy of the power system to meet load off grid, such as at a military forward operating base. The model alternately uses two types of load data from government agencies, simulated and observed, to assess the effects of these inputs. Because the latter data set contains errors and is incomplete, we detail the process of cleaning and imputing it to provide a year’s worth in hourly increments for two forward operating bases in Afghanistan. We then construct an approximation of a realistic 600-soldier camp load from the full year of observed data. We compare the design and dispatch output from the optimization model using the simulated and constructed (observed) data sets and demonstrate that the results can differ. We investigate the characteristics of load that influence the optimization model’s behavior regarding the design and dispatch strategy and show that mean load has a more pronounced effect than its shape. In addition, the photovoltaic cells are often used to help the generators run more efficiently, especially under load variability.

Renewable energy technologies are becoming increasingly important due to their cost-competitiveness, and because of enhanced climate concerns. We demonstrate the capabilities of an integer-programming optimization model that minimizes capital (investment) and operational costs, and utility charges, while adhering to system sizing constraints, demand requirements, and interoperability characteristics of the systems chosen. The model recommends an optimally sized mix of renewable energy, conventional generation, and energy storage technologies, while simultaneously optimizing the corresponding dispatch strategy. Our case studies explore several venues, i.e., a small campus and a local hospital, with complex utility rate tariffs, multi-technology integration opportunities, and incentives for renewable power production. Using an optimization model, versus applying rules of thumb, can produce millions of dollars in savings over a 25-year time horizon and result in thousands of kilowatts of installed renewable energy.

Optimal steady-state dispatch of a stand-alone hybrid power system determines a fuel-minimizing distribution strategy while meeting a forecasted demand over six months to a year. Corresponding optimization models that integrate hybrid technologies such as batteries, diesel generators, and photovoltaics with system interoperability requirements are often large, nonconvex, nonlinear, mixed-integer programming problems that are difficult to solve even using the most state-of-the-art algorithms. The rate-capacity effect of a battery causes capacity to vary nonlinearly with discharge current; omitting this effect simplifies the model, but leads to over-estimation of discharge capabilities. We present a physics-based set of integer-linear constraints to model batteries in a hybrid system for a steady-state dispatch optimization problem that minimizes fuel use. Starting with a nonlinear set of constraints, we empirically derive linearizations and then compare them to a commonly used set of constraints that assumes a constant voltage and neglects rate-capacity. Numerical results demonstrate that assuming a fixed voltage and capacity may lead to over-estimating discharge quantities by up to 16% compared to our overestimations of less than 1%.

Combined heat and power (CHP), distributed generation (DG) technologies have the potential to provide economic savings to commercial building owners in certain markets, if the system is appropriately configured, sized, and operated. Numerous optimization models exist for determining the design and dispatch of a DG system, and some require a great deal of time and computing power to determine building-market scenarios for which the optimal solution includes the acquisition of CHP technologies. Thus, it is beneficial to identify which scenarios are likely to be economically viable prior to solving an optimization model that determines the lowest-cost system design and dispatch. Accordingly, we derive conditions for the economic viability of a CHP DG technology by comparing the total operational savings afforded by the technology to its total installed cost. We demonstrate these conditions numerically in eight distinct scenarios that include the installation of a fuel cell-based CHP system for various building types and energy markets. Using these scenarios, we examine the energy, emissions, operations and maintenance, and peak demand savings provided by the DG system, and determine which scenarios are likely to result in total savings that exceed the total installed cost. Results indicate that the combination of building type, energy market, and system design and dispatch in a given scenario have a significant impact on the economic viability of the CHP system.

The distributed generation (DG) of combined heat and power (CHP) for commercial buildings is gaining increased interest, yet real-world installations remain limited. This lack of implementation is due, in part, to the challenging economics associated with volatile utility pricing and potentially high system capital costs. Energy technology application analyses are also faced with insufficient knowledge regarding how to appropriately design (i.e., configure and size) and dispatch (i.e., operate) an integrated CHP system. Existing research efforts to determine a minimum-cost-system design and dispatch do not consider many dynamic performance characteristics of generation and storage technologies. Consequently, we present a mixed-integer nonlinear programming (MINLP) model that prescribes a globally minimum cost system design and dispatch, and that includes off-design hardware performance characteristics for CHP and energy storage that are simplified or not considered in other models. Specifically, we model the maximum turn-down, start up, ramping, and part-load efficiency of power generation technologies, and the time-varying temperature of thermal storage technologies. The consideration of these characteristics can be important in applications for which system capacity, building demand, and/or utility guidelines dictate that the dispatch schedule of the devices varies over time. We demonstrate the impact of neglecting system dynamics by comparing the solution prescribed by a simpler, linear model with that of our MINLP for a case study consisting of a large hotel, located in southern Wisconsin, retrofitted with solid-oxide fuel cells (SOFCs) and a hot water storage tank. The simpler model overestimates the SOFC operational costs and, consequently, underestimates the optimal SOFC capacity by 15%.