Bi-level multi-objective optimization of the structure and attitude for space solar power station

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method.

We propose a procedure to construct evolutionary bilevel optimization algorithms based on recent theoretical advances that have established connections between bilevel optimization and multiobjective optimization. In the proposed procedure, a new algorithm is defined by integrating an evolutionary multiobjective optimization algorithm with a partial order that is compatible with bilevel optimization. The advantages of the procedure include the ability to harness the methodology of evolutionary multiobjective optimization for bilevel optimization and to systematically develop new algorithms for single-objective and multiobjective bilevel optimization. No regularity assumptions are used, which ensures maximal applicability of the optimization algorithms constructed by the procedure. The necessary theoretical foundation is developed and the steps of the procedure are illustrated with an example.

Bilevel and multi-objective optimization methods are often useful to spatially target agri-environmental policy throughout a watershed. This type of problem is complex and is comprised of a number of practicalities: (i) a large number of decision variables, (ii) at least two inter-dependent levels of optimization between policy makers and policy followers, and (iii) uncertainty in decision variables and problem parameters. Given agricultural and economic data from the Raccoon watershed in central Iowa, we formulate a bilevel multi-objective optimization problem that accommodates objectives of both policy makers and farmers. The solution procedure then explicitly accounts for the nested nature of farm-level management decisions in response to agri-environmental policy incentives constructed by policy makers. We specifically examine the spatial targeting of a fertilizer-reduction incentive policy while seeking to maximize farm-level productivity while generating mandated water quality improvements using this framework. We test three different evolutionary optimization algorithms - m-BLEAQ, NSGA-II, and SPEA2 - and show that m-BLEAQ is well suited for handling the bilevel optimization problems and the considered practicalities.

Heavy-duty commercial electric vehicle (HDEV) charging stations, such as for freight trucks, must handle large peak power demands. Installing on-site energy storage can reduce the peak charging demand to avoid expensive and oversized utility-managed distribution equipment. To ensure optimal design of charging infrastructure, the trade-off between energy storage size and grid equipment ratings should be considered. This paper presents a bi-level multi-objective optimization framework to discover Pareto optimal designs, under the constraint of optimally sized power electronic converters and realistic power loss models. Under these considerations, the bi-level approach can greatly simplify the design process by breaking up charging station optimization into a system-level problem and multiple converter-level problems. Using industry-based HDEV arrival times and charging conditions, this bi-level approach is demonstrated for a 9port charging station. The resulting Pareto front showcases equipment sizing trade-offs that are necessary for informed charging infrastructure development decisions. The bi-level optimization Pareto front is compared the Pareto fronts of traditional, fixed efficiency converter models.

Bi-level optimization design strategy for compressed air energy storage of a combined cooling, heating, and power system

The Space Solar Power Station/Satellite (SSPS) is a large-scale space-borne facility intended for the direct collection and conversion of solar energy in the extra-stratospheric region. The optimization of its light collection and conversion (LCC) structures, analysis of dynamic characteristics, and design of attitude control systems represent core technical bottlenecks impeding the advancement of SSPS. To address these issues, this study investigates a novel conceptual line-focusing SSPS. Firstly, a multi-objective collaborative optimization model is developed to optimize the structural parameters of the concentrator and photovoltaic (PV) array. Subsequently, based on the optimized parameters, a coupled multi-body dynamic model is formulated, incorporating gravity-gradient torque and other space-borne disturbance factors. Finally, a distributed Proportional–Integral–Derivative (PID) controller is proposed to achieve three-axis attitude stabilization of the SSPS. Simulation results demonstrate that the light collection efficiency achieves 81.9% with a power density of 4792.24 W/m2; concurrently, a balance between the geometric parameters of the LCC system and the aforementioned key performance indicators is attained, and the proposed controller possesses favorable anti-disturbance performance.

Summary form only given. Japan Aerospace Exploration Agency (JAXA) has been conducting studies on space solar power systems (SSPS) using microwave and laser beams for years since FY1998 organizing a special committee and working groups. In case of microwave based SSPS, the solar energy must be converted to electricity and then converted to a microwave beam. The on-ground rectifying antenna would collect the microwave beam and convert it to electricity to connect to commercial power grids. In the laser based SSPS, a solar condenser equipped with lenses or mirrors and laser-generator would be put into orbit. A laser beam would be sent to an Earth-based hydrogen generating device. JAXA is proposing a roadmap that consists of a stepwise approach to achieve commercial SSPS in 20-30 years. The first step is tens of kW class technology demonstration satellite to demonstrate microwave or laser power transmission. The second step is to demonstrate robotic assembly of 10 MW class large scale flexible structure in space on ISS co-orbit. The third step is to build a prototype SSPS in GEO. The final step is to build commercial GW class SSPS in GEO. This paper presents the results of these study effort of JAXA and from those results, identifies the most promising SSPS concepts, including their key technologies.

Electronic countermeasure (ECM) has become one of the most significant factors in modern warfare, in the course of combat, the electronic jamming allocation tasks need to be flexibly adjusted with the change of combat stage, which puts forward higher requirements for the modeling and solution method of this kind of problems. To solve the ECM dynamic weapon target assignment (ECM-DWTA) problem, a hybrid multi-target bi-level programming model is established. The upper level takes the sum of the electronic jamming effects in the whole combat stage as an optimization objective, and locally optimizes the ECM weapon (ECM-WP) assignment scheme in each stage. The lower level takes the importance expectation value of the target subjected to interference and combat consumption as double optimization objectives to globally optimize the ECM-WP assignment scheme. Focus on solving this complex model, a hybrid multi-objective bi-level interactive fuzzy programming algorithm (HMOBIF) is proposed, in this method, exponential membership function is used to describe the satisfaction degree of each level. When solving the multi-objective optimization problem composed of membership functions in the upper and lower levels, we use the MOEA/D algorithm to obtain the Pareto Front (PF) solution set, and then each solution in PF is evaluated and selected by the TOPSIS multi-criteria evaluation method. This local and global interactive optimization process of bi-level model is actually the process of executing observation-orientation-decision-action loop in practical combat. According to the current example, we conduct numerical simulation on the parameters in the model and obtain the parameter values suitable for the model solution. The computational experiments on different scale ECM-DWTA problems show that HMOBIF method is superior to four bi-level programming algorithms in terms of performance index, and can better solve ECM-DWTA problems.

A multi-objective Bi-level leader-follower joint optimization for concurrent design of product family and assembly system

The development of space power engineering is one of the well-known lines in rocket and space science and innovative technologies which attracts the attention of many scientists and researchers. Engineering solutions in space-based solar power plant design and wireless space-to-Earth and satellite-to-satellite power transmission and power spacecraft control methods have been substantiated theoretically to sufficient depth. However, despite this, there is a need to improve methods for and approaches to the development of an optimal design methodology for power spacecraft. A way to improve existing approaches to the development of space-based solar power plants and power satellites may be the use of mobile control methods in the development of an attitude and orbit control system. Such methods allow one to reduce power consumption for control operations. The goal of this paper is to study the features of mobile control and construct a methodology for the development of solar power satellites’ attitude and orbit control system (AOCS) using mobile control algorithms. The paper considers the features of mobile control algorithm synthesis for the attitude control and stabilization of solar power spacecraft (solar power plants and power satellites). Power spacecraft control tasks are classified, and the expediency of using mobile control methods is justified. An analysis is made for the stability problem that arises in controlling power spacecraft with flexible elements. The paper presents methodological recommendations on determining the AOCS design parameters for space-based solar power plants and power spacecraft for wireless satellite-to-satellite power transmission. This methodology may be used in power satellite development.

Construction of Solar Power Satellites from lunar resources

The space-based solar power (SBSP) concept was introduced during the 1970s. However, the technology difficulties during that period stalled its development. Nowadays, SBSP has regained the interest of researchers as it is considered a reliable source of green energy. A solar power harvesting method using a spacecraft formation in a Low Earth Orbit has been introduced in the literature. Each spacecraft in the formation harvests the solar energy and transmits the power to the ground station simultaneously. However, high relative speed within the spacecraft, and also within the spacecraft and the earth, causes nonhomogeneous doppler spread during solar power transmission. This reduces SBSP power transmission efficiency. In this paper, the impact of Doppler spread variation between two spacecraft in the formation, and also with respect to the receiver (leader satellite or ground station) on the power transmission performance is investigated by considering different formation configurations.

Bi-level optimal sizing and energy management of hybrid electric propulsion systems

From fixed points to optimum regions: AI-NSGA-II framework for high-recovery, low-energy brackish water RO.

In this topic, bi-level optimization methods based on non-deterministic approaches are presented with emphasis on solving process synthesis problems, in large-scale problems, where determinist approaches can lead to non-optimal solutions, with huge computational effort. When integer and real variables are present, bi-level approaches can present good solutions in reasonable computational times. Normally, binary variables are used when decisions must be taken, such as whether to use a piece of equipment, whether a process stream must be split, etc. Real variables are the other design variables. Some case studies are used to show the applicability of these methods. The chosen examples are related to process synthesis and heat integration processes, with distinct applications.