This work presents a critical review of the use of exergy based control and optimization for efficiency improvements in energy networks, with a background of exergy based analysis given for context. Over the past three decades, a number of studies using exergy were conducted to gain a performance advantage for high energy consumption systems and networks. Due to their complexity and the increased scale of the systems, the opportunity to misuse energy inevitability leads to inefficient operations. The studies accomplished in this area are grouped into either control or optimization to highlight each method's ability to minimize system irreversibilities that lead to exergy destruction. The exergy based optimization and control studies featured demonstrate substantial improvements (as high as 40%) over traditional methods based on the first law of thermodynamics. This paper reviews the work completed in the area of exergy based optimization and control as of the end of September 2019, outlines the progress made, and identifies specific areas where future work can advance this area of study. A relatively small amount of publications are available compared to other fields, with most work occurring in the area of exergy based multi-objective optimization.

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A microgrid consists of electrical generation sources, energy storage assets, loads, and the ability to function independently, or connect and share power with other electrical grids. Thefocus of this work is on the behavior of a microgrid, with both diesel generator and photovoltaic resources, whose heating or cooling loads are influenced by local meteorological conditions. Themicrogrid's fuel consumption and energy storage requirement were then examined as a function of the atmospheric conditions used by its energy management strategy (EMS). A fuel-optimal EMS, able to exploit meteorological forecasts, was developed and evaluated using a hybrid microgrid simulation. Weather forecast update periods ranged from 15 min to 24 h. Four representative meteorological sky classifications (clear, partly cloudy, overcast, or monsoon) were considered. Forall four sky classifications, fuel consumption and energy storage requirements increased linearly with the increasing weather forecast interval. Larger forecast intervals lead to degraded weather forecasts, requiring more frequent charging/discharging of the energy storage, increasing both the fuel consumption and energy storage design requirements. The significant contributions of this work include the optimal EMS and an approach for quantifying the meteorological forecast effects on fuel consumption and energy storage requirements on microgrid performance. The findings of this study indicate that the forecast interval used by the EMS affected both fuel consumption and energy storage requirements, and that the sensitivity of these effects depended on the 24-hour sky conditions.

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To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a methodology which was simulated the energy dynamics of a ground vehicle in parallel to running predictive neural network (NN) based predictive algorithms to address two separate research questions: (1) can energy and exergy flow characterization be developed at a future point in time, and (2) can we use the predictive algorithms to extend the energy and exergy flow characterization and derive operational intelligence, used to inform our control based algorithms or provide optimized recommendations to a battlefield commander in real-time. Using our predictive algorithms we confirmed that the future energy and exergy flow characterizations could be generated using the NNs, which was validated through simulation using two separately created datasets, one for training and one for testing. We then used the NNs to implement a model predictive control (MPC) framework to flexibly operate the vehicles thermal coolant loop (TCL), subject to exergy destruction. In this way we could tailor the performance of the vehicle to accommodate a more mission effective solution or a less energy intensive solution. The MPC resulted in a more effective solution when compared to six other simulated conditions, which consumed less exergy than two of the six cases. Our results indicate that we can derive operational intelligence from the predictive algorithms and use it to inform a model predictive control (MPC) framework to reduce wasted energy and exergy destruction subject to the variable operating conditions.

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We consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy storage capacity. An energy management system (EMS) can prevent load and energy shedding during stress conditions while minimizing fuel consumption. This is important when the loads are high priority and fuel is in short supply, such as in disaster relief and military applications. One example is a low-power, provisional microgrid deployed temporarily to service communication loads immediately after an earthquake. Due to changing circumstances, the power grid may be required to service additional loads for which its storage and generation were not originally designed. An EMS that uses forecasted load and generation has the potential to extend the operation, enhancing the relief objectives. Our focus was to explore how using forecasted loads and PV generation impacts energy management strategy performance. A microgrid EMS was developed exploiting PV and load forecasts to meet electrical loads, harvest all available PV, manage storage and minimize fuel consumption. It used a Model Predictive Control (MPC) approach with the instantaneous grid storage state as feedback to compensate for forecasting errors. Four scenarios were simulated, spanning a stressed and unstressed grid operation. The MPC approach was compared to a rule-based EMS that did not use load and PV forecasting. Both algorithms updated the generator’s power setpoint every 15 min, where the grid’s storage was used as a slack asset. While both methods had similar performance under unstressed conditions, the MPC EMS showed gains in storage management and load shedding when the microgrid was stressed. When the initial storage was low, the rule-based EMS could not meet the load requirements and shed 16% of the day’s electrical load. In contrast, the forecast-based EMS managed the load requirements for this scenario without shedding load or energy. The EMS sensitivity to forecast error was also examined by introducing load and PV generation uncertainty. The MPC strategy successfully corrected the errors through storage management. Since weather affects both PV energy generation and many types of electrical loads, this work suggests that weather forecasting advances can improve remote microgrid performance in terms of fuel consumption, load satisfaction, and energy storage requirements.

The majority of air and ground vehicle systems are reliant on specialized diesel fuel. This reliance increases the likelihood that operations may be operating in an energy constrained or contested environment given the state of international relations between global energy providers and consumers. Such a vulnerability has the potential to reduce operational effectiveness or efficiency if logistical supply chains were interrupted or impeded. The most effective and efficient methodology to reduce reliance on specialized diesel fuel is to hybridize our energy and power (E&P) systems, and support more diverse E&P solutions including renewable energy generation (photovoltaic (PV) arrays, wind generation, wave energy converters), nuclear, or decaying isotopes. In this paper/presentation, we present our advances in developing a set of predictive artificial intelligence and machine learning (AI/ML) algorithms that forecast E&P capabilities of a photovoltaic array indirectly and directly. These milestones are a product of two separate types of AI/ML approaches: (1) developing AI/ML based algorithms that predict ambient and panel temperature from various atmosphericbased sensor data which can then be used in combination with an irradiance profile and a MATLAB Simulink model to predict the E&P capabilities of the PV array (indirect method), and (2) developing AI/ML which predicts the resulting E&P capabilities of the PV array, using various atmospheric-based sensor data (direct method).