A review of optimization approaches for controlling water-cooled central cooling systems

Buildings consume a large amount of energy across all sectors of society, and a large proportion of building energy is used by HVAC systems to provide a comfortable and healthy indoor environment. In medium and large-size buildings, the central cooling system accounts for a major share of the energy consumption of the HVAC system. Improving the cooling system efficiency has gained much attention as the reduction of cooling system energy use can effectively contribute to environmental sustainability. The control and operation play an important role in central cooling system energy efficiency under dynamic working conditions. It has been proven that optimization of the control of the central cooling system can notably reduce the energy consumption of the system and mitigate greenhouse gas emissions. In recent years, numerous studies focus on this topic to improve the performance of optimal control in different aspects (e.g., energy efficiency, stability, robustness, and computation efficiency). This paper provides an up-to-date overview of the research and development of optimization approaches for controlling water-cooled central cooling systems, helping readers to understand the new significant trends and achievements in this area. The optimization approaches have been classified as system-model-based and data-based. In this paper, the optimization methodology is introduced first by summarizing the key decision variables, objective function, constraints, and optimization algorithms. The principle and performance of various optimization approaches are then summarized and compared according to their classification. Finally, the challenges and development trends for optimal control of water-cooled central cooling systems are discussed

Data analytics for performance evaluation under uncertainties applied to an industrial refrigeration plant

Artificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems. In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system. The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows comparing the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.Peer ReviewedPostprint (published version

Optimal Design of a Solar Assisted Cooling System

Rapid development around the globe is fairly associated with huge consumption of energy; regardless of the continuous attempts of exploiting renewable energy resources, further investigations in renewable energy involvement in comfort cooling appears to be interesting. District Cooling Systems (DCS) are chilled water based systems operate to provide comfort cooling. DCS consists of chilled water plant, chilled water distribution network and energy transfer station(s), where Thermal Energy Storage system (TES) might be included alongside with DCS as auxiliary components(s). Although typical DCS are fully dependent on fossil fuel as source of energy in their operation, providing comfort cooling is considered as a necessity in some regions of the globe. Such circumstances highlight the imperative of examining other sources of energy, such as renewable energy. One think there is no better alternative of energy resource problem than solar energy, specifically the science of converting heat into cool. Researches in the field of Solar Assisted Cooling systems (SAC) designated typical components of solar assisted cooling system to be solar collector(s) and absorption chiller(s); where TES and water boiler utilized as auxiliary components. In comparison to conventional cooling systems, SAC systems have the advantages of renewable energy utilization beside the correlation of high availability of solar energy with the high demand of comfort cooling. Yet, their relatively high investment costs introduces barriers toward their implementation; thus, the contribution of this research is realized in mathematically modeling SAC system and obtaining the optimal design of such system with the aim of minimizing the investment and operational costs. The problem is modeled as Mixed Integer Linear Problem (MILP) and the optimization of the model is implemented using CPLEX optimizer. The optimized solution specify the optimal area of the solar collector, size of absorption chiller, size and existence of chilled and hot water storage tanks, and the auxiliary boiler

Energy analysis of a Micro-CHP demonstration facility

Cooling, Heating, and Power (CHP) systems have been around for decades, but systems that utilize 20 kW or less, designated as Micro-CHP, are relatively new. Micro-CHP systems show the most promise for a distributed generation scheme to decentralize the national energy grid. A demonstration site has been constructed at Mississippi State University to show the advantages of these systems. This study is designed to evaluate the performance of a Micro-CHP system and a conventional high-efficiency system. Performance and cost factors can be evaluated for the demonstration site operating under either the CHP system or the conventional system. These results are computed from an energy analysis on collected data. This dissertation introduces a new comparison factor to examine different CHP systems. This new factor is called the System Energy Transfer Ratio (SETR). Other considerations in this study include an extensive literature survey that reviews CHP systems, their components, modeling, and other topics concerning CHP systems operation. In addition, the demonstration facility will be discussed in detail presenting the various components and instrumentation. Furthermore, the energy analysis will be presented, examining the equations used to evaluate the raw data from the demonstration site. An uncertainty analysis will be presented for the experimental results. Raw data was collected for 7 months to present the following results. The combined cycle efficiency from the demonstration site was averaged at 29%. Maximum combined cycle efficiency was evaluated at 58%. The average combined boiler and engine cost, per hour of operation, is shown as $1.8 for heating and$3.9 for cooling. The cooling technology used, an absorption chiller, has been shown to exhibit an average COP of 0.27. The proposed SETR for the demonstration site is 22% and 15%, for heating and cooling, respectively. The conventional high-efficiency system, during cooling mode, was shown to have a COP of 4.7 with a combined cooling and building cost of $0.2/hour of operation. During heating mode, the conventional system had an efficiency of 47$0.28/hour of operation

Energy analysis of a Micro-CHP demonstration facility

Cooling, Heating, and Power (CHP) systems have been around for decades, but systems that utilize 20 kW or less, designated as Micro-CHP, are relatively new. Micro-CHP systems show the most promise for a distributed generation scheme to decentralize the national energy grid. A demonstration site has been constructed at Mississippi State University to show the advantages of these systems. This study is designed to evaluate the performance of a Micro-CHP system and a conventional high-efficiency system. Performance and cost factors can be evaluated for the demonstration site operating under either the CHP system or the conventional system. These results are computed from an energy analysis on collected data. This dissertation introduces a new comparison factor to examine different CHP systems. This new factor is called the System Energy Transfer Ratio (SETR). Other considerations in this study include an extensive literature survey that reviews CHP systems, their components, modeling, and other topics concerning CHP systems operation. In addition, the demonstration facility will be discussed in detail presenting the various components and instrumentation. Furthermore, the energy analysis will be presented, examining the equations used to evaluate the raw data from the demonstration site. An uncertainty analysis will be presented for the experimental results. Raw data was collected for 7 months to present the following results. The combined cycle efficiency from the demonstration site was averaged at 29%. Maximum combined cycle efficiency was evaluated at 58%. The average combined boiler and engine cost, per hour of operation, is shown as $1.8 for heating and$3.9 for cooling. The cooling technology used, an absorption chiller, has been shown to exhibit an average COP of 0.27. The proposed SETR for the demonstration site is 22% and 15%, for heating and cooling, respectively. The conventional high-efficiency system, during cooling mode, was shown to have a COP of 4.7 with a combined cooling and building cost of $0.2/hour of operation. During heating mode, the conventional system had an efficiency of 47$0.28/hour of operation

Development of robust building energy demand-side control strategy under uncertainty

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.Ph.D.Committee Chair: Augenbroe, Gofried; Committee Member: Brown, Jason; Committee Member: Jeter, Sheldon; Committee Member: Paredis,Christiaan; Committee Member: Sastry, Chellur

Investigation of Some Self-Optimizing Control Problems for Net-Zero Energy Buildings

Green buildings are sustainable buildings designed to be environmentally responsible and resource efficient. The Net-Zero Energy Building (NZEB) concept is anchored on two pillars: reducing the energy consumption and enhancing the local energy generation. In other words, efficient operation of the existing building equipment and efficient power generation of building integrated renewable energy sources are two important factors of NZEB development. The heating, ventilation and air conditioning (HVAC) systems are an important class of building equipment that is responsible for large portion of building energy usage, while the building integrated photovoltaic (BIPV) system is well received as the key technology for local generation of clean power. Building system operation is a low-investment practice that aims low operation and maintenance cost. However, building HVAC and BIPV are systems subject to complicated intrinsic processes and highly variable environmental conditions and occupant behavior. Control, optimization and monitoring of such systems desire simple and effective approaches that require the least amount of model information and the use of smallest number but most robust sensor measurements. Self-optimizing control strategies promise a competitive platform for control, optimization and control integrated monitoring for building systems, and especially for the development of cost-effective NZEB. This dissertation study endorses this statement with three aspects of work relevant to building HVAC and BIPV, which could contribute several small steps towards the ramification of the self-optimizing control paradigm. This dissertation study applies self-optimizing control techniques to improve the energy efficiency of NZEB from two aspects. First, regarding the building HVAC efficiency, the dither based extremum seeking control (DESC) scheme is proposed for energy efficient operation of the chilled-water system typically used in the commercial building ventilation and air conditioning (VAC) systems. To evaluate the effectiveness of the proposed control strategy, Modelica based dynamic simulation model of chilled water chiller-tower plant is developed, which consists of a screw chiller and a mechanical-draft counter-flow wet cooling tower. The steady-state performance of the cooling tower model is validated with the experimental data in a classic paper and good agreement is observed. The DESC scheme takes the total power consumption of the chiller compressor and the tower fan as feedback, and uses the fan speed setting as the control input. The inner loop controllers for the chiller operation include two proportional-integral (PI) control loops for regulating the evaporator superheat and the chilled water temperature. Simulation was conducted on the whole dynamic simulation model with different environment conditions. The simulation results demonstrated the effectiveness of the proposed ESC strategy under abrupt changes of ambient conditions and load changes. The potential for energy savings of these cases are also evaluated. The back-calculation based anti-windup ESC is also simulated for handling the integral windup problem due to actuator saturation. Second, both maximum power point tracking (MPPT) and control integrated diagnostics are investigated for BIPV with two different extremum seeking control strategies, which both would contribute to the reduction of the cost of energy (COE). In particular, the Adaptive Extremum Seeking Control (AESC) is applied for PV MPPT, which is based on a PV model with known model structure but unknown nonlinear characteristics for the current-voltage relation. The nonlinear uncertainty is approximated by a radial basis function neural network (RBFNN). A Lyapunov based inverse optimal design technique is applied to achieve parameter estimation and gradient based extremum seeking. Simulation study is performed for scenarios of temperature change, irradiance change and combined change of temperature and irradiance. Successful results are observed for all cases. Furthermore, the AESC simulation is compared to the DESC simulation, and AESC demonstrates much faster transient responses under various scenarios of ambient changes. Many of the PV degradation mechanisms are reflected as the change of the internal resistance. A scheme of detecting the change of PV internal shunt resistance is proposed using the available signals in the DESC based MPPT with square-wave dither. The impact of the internal resistance on the transient characteristics of step responses is justified by using the small-signal transfer function analysis. Simulation study is performed for both the single-string and multi-string PV examples, and both cases have demonstrated successful results. Monotonic relationship between integral error indices and the shunt internal resistance is clearly observed. In particular, for the multi-string, the inter-channel coupling is weak, which indicates consistent monitoring for multi-string operation. The proposed scheme provides the online monitoring ability of the internal resistance condition without any additional sensor, which benefits further development of PV degradation detection techniques

Optimal operation strategies of multi-energy systems integrated with liquid air energy storage using information gap decision theory

In this paper, a framework of multi-energy system (MES) integrating with a liquid air energy storage (LAES) system was proposed. LAES, where liquid air works as an energy storage media, is a powerful and eco-friendly technology for storing renewable energy resources and reducing grid curtailment. Considering the characteristics of LAES (i.e. cold and heat circulation), the incorporation of LAES system into the Combined Cooling, Heating and Power system can achieve integrated use of energy and effectively save energy. Moreover, the prices of electricity will affect the overall cost of the MES. In other words, the decision-makers of the MES need to consider the uncertainty of electricity prices when making power dispatching decisions. To model the uncertainty of electricity prices, the information gap decision theory method was used to study power dispatching strategies of the MES. Three different strategies were proposed, including risk-neutral, risk-averse and risk-taker. In addition, demand response algorithms were used to study load transfer strategies. The results show that the demand responses of the three strategies are effective in terms of load transfer and cost saving. The total operation cost in the risk-neutral strategy with demand response can be 6.82% less than that without demand response; In the risk-taker strategy with demand response, the allowable grid electricity price is reduced by 25.24% when the opportunity cost drops by $8,000, and 23.32% without demand response. With additional robustness cost, the acceptable price change ratio using demand response is 21.91% in the risk-averse strategy, and 20.04% without demand response

Sustainability Assessment of Community Scale Integrated Energy Systems: Conceptual Framework and Applications

abstract: One of the key infrastructures of any community or facility is the energy system which consists of utility power plants, distributed generation technologies, and building heating and cooling systems. In general, there are two dimensions to “sustainability” as it applies to an engineered system. It needs to be designed, operated, and managed such that its environmental impacts and costs are minimal (energy efficient design and operation), and also be designed and configured in a way that it is resilient in confronting disruptions posed by natural, manmade, or random events. In this regard, development of quantitative sustainability metrics in support of decision-making relevant to design, future growth planning, and day-to-day operation of such systems would be of great value. In this study, a pragmatic performance-based sustainability assessment framework and quantitative indices are developed towards this end whereby sustainability goals and concepts can be translated and integrated into engineering practices. New quantitative sustainability indices are proposed to capture the energy system environmental impacts, economic performance, and resilience attributes, characterized by normalized environmental/health externalities, energy costs, and penalty costs respectively. A comprehensive Life Cycle Assessment is proposed which includes externalities due to emissions from different supply and demand-side energy systems specific to the regional power generation energy portfolio mix. An approach based on external costs, i.e. the monetized health and environmental impacts, was used to quantify adverse consequences associated with different energy system components. Further, this thesis also proposes a new performance-based method for characterizing and assessing resilience of multi-functional demand-side engineered systems. Through modeling of system response to potential internal and external failures during different operational temporal periods reflective of diurnal variation in loads and services, the proposed methodology quantifies resilience of the system based on imposed penalty costs to the system stakeholders due to undelivered or interrupted services and/or non-optimal system performance. A conceptual diagram called “Sustainability Compass” is also proposed which facilitates communicating the assessment results and allow better decision-analysis through illustration of different system attributes and trade-offs between different alternatives. The proposed methodologies have been illustrated using end-use monitored data for whole year operation of a university campus energy system.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201