Space power distribution system technology. Volume 1: Reference EPS design

The multihundred kilowatt electrical power aspects of a mannable space platform in low Earth orbit is analyzed from a cost and technology viewpoint. At the projected orbital altitudes, Shuttle launch and servicing are technically and economically viable. Power generation is specified as photovoltaic consistent with projected planning. The cost models and trades are based upon a zero interest rate (the government taxes concurrently as required), constant dollars (1980), and costs derived in the first half of 1980. Space platform utilization of up to 30 years is evaluated to fully understand the impact of resupply and replacement as satellite missions are extended. Such lifetimes are potentially realizable with Shuttle servicing capability and are economically desirable

Reinforcement learning for power scheduling in a grid-tied pv-battery electric vehicles charging station

Grid-tied renewable energy sources (RES) based electric vehicle (EV) charging stations are an example of a distributed generator behind the meter system (DGBMS) which characterizes most modern power infrastructure. To perform power scheduling in such a DGBMS, stochastic variables such as load profile of the charging station, output profile of the RES and tariff profile of the utility must be considered at every decision step. The stochasticity in this kind of optimization environment makes power scheduling a challenging task that deserves substantial research attention. This dissertation investigates the application of reinforcement learning (RL) techniques in solving the power scheduling problem in a grid-tied PV-powered EV charging station with the incorporation of a battery energy storage system. RL is a reward-motivated optimization technique that was derived from the way animals learn to optimize their behavior in a new environment. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. This study developed and evaluated the feasibility of two RL algorithms, namely, an asynchronous Q-learning algorithm and an advantage actor-critic (A2C) algorithm, in performing power scheduling in the EV charging station under static conditions. To assess the performances of the proposed algorithms, the conventional Q-learning and actor-critic algorithm were implemented to compare their global cost convergence and their learning characteristics. First, the power scheduling problem was expressed as a sequential decision-making process. Then an asynchronous Q-learning algorithm was developed to solve it. Further, an advantage actor-critic (A2C) algorithm was developed and was used to solve the power scheduling problem. The two algorithms were tested using a 24-hour load, generation and utility grid tariff profiles under static optimization conditions. The performance of the asynchronous Q-learning algorithm was compared with that of the conventional Q-learning method in terms of the global cost, stability and scalability. Likewise, the A2C was compared with the conventional actor-critic method in terms of stability, scalability and convergence. Simulation results showed that both the developed algorithms (asynchronous Q-learning algorithm and A2C) converged to lower global costs and displayed more stable learning characteristics than their conventional counterparts. This research established that proper restriction of the action-space of a Q-learning algorithm improves its stability and convergence. It was also observed that such a restriction may come with compromise of computational speed and scalability. Of the four algorithms analyzed, the A2C was found to produce a power schedule with the lowest global cost and the best usage of the battery energy storage system

HopScotch - a low-power renewable energy base station network for rural broadband access

The provision of adequate broadband access to communities in sparsely populated rural areas has in the past been severely restricted. In this paper, we present a wireless broadband access test bed running in the Scottish Highlands and Islands which is based on a relay network of low-power base stations. Base stations are powered by a combination of renewable sources creating a low cost and scalable solution suitable for community ownership. The use of the 5~GHz bands allows the network to offer large data rates and the testing of ultra high frequency ``white space'' bands allow expansive coverage whilst reducing the number of base stations or required transmission power. We argue that the reliance on renewable power and the intelligent use of frequency bands makes this approach an economic green radio technology which can address the problem of rural broadband access

Optimal energy management for a grid-tied solar PV-battery microgrid: A reinforcement learning approach

There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases

UK-China collaborative study on low carbon technology transfer: final report

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Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS

Investigating Lifecycle Costs of Optimized Battery-Photovoltaic Systems on a Forward Operating Base

The purpose of this research was to investigate the total life-cycle cost of using utility-scale battery systems to increase the energy efficiency of forward operating bases, thereby reducing the burden of diesel fuel logistics. Specifically, this thesis answered three research questions addressing optimal sizing for various battery types connected with photovoltaic grids, logistical parameters directly impacting total cost, and the cost of increasing the energy resilience of the network. The research questions were answered through a review of literature, modeling, and data analysis. The model determines an optimal size and area for a Vanadium redox flow, Lithium-ion, or Lead-acid battery system, combined with a photovoltaic array, over 5, 10, and 20 years. The optimal Lead-acid battery system was the least expensive, with a 20-year lifecycle system of 142.1 MWh battery and 30.9-acre photovoltaic array costing $13.1M per year. However, after including transportation costs, operations and maintenance, and salvage values, Lithium-ion and Vanadium flow appear to be more cost effective. With a 20-year life-cycle, Lithium-ion and Vanadium redox flow batteries were the most cost-effective option, for the theoretically modeled Alpha forward operating base, with an equivalent annual cost of$24.1M per year and $24.8M per year, respectively. When excluding salvage value from the total cost, both systems cost$25.2M per year and $25.7M per year, respectively. Lead-acid costs for 20 years were$28.4M per year. A breakdown of all costs associated with the final value of each battery system is included in the results. Recommendations on implementation of a battery-photovoltaic system on a forward operating base are discussed. Shortfalls of each technology are also discussed

Advanced Integrated Power and Attitude Control System (IPACS) study

Integrated Power and Attitude Control System (IPACS) studies performed over a decade ago established the feasibility of simultaneously satisfying the demands of energy storage and attitude control through the use of rotating flywheels. It was demonstrated that, for a wide spectrum of applications, such a system possessed many advantages over contemporary energy storage and attitude control approaches. More recent technology advances in composite material rotors, magnetic suspension systems, and power control electronics have triggered new optimism regarding the applicability and merits of this concept. This study is undertaken to define an advanced IPACS and to evaluate its merits for a space station application. System and component designs are developed to establish the performance of this concept and system trade studies conducted to examine the viability of this approach relative to conventional candidate systems. It is clearly demonstrated that an advanced IPACS concept is not only feasible, but also offers substantial savings in mass and life-cycle cost for the space station mission