Bi-Level Non-deterministic Optimization Methods Applied in Process Synthesis

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.

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

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

In edge computing, suppressing data size is a challenge for machine learning models that perform complex tasks such as autonomous driving, in which computational resources (speed, memory size and power) are limited. Efficient lossy compression of matrix data has been introduced by decomposing it into the product of an integer and real matrices. However, its optimisation is difficult as it requires simultaneous optimisation of an integer and real variables. In this paper, we improve this optimisation by utilising recently developed black-box optimisation (BBO) algorithms with an Ising solver for binary variables. In addition, the algorithm can be used to solve mixed-integer programming problems that are linear and non-linear in terms of real and integer variables, respectively. The differences between the choice of Ising solvers (simulated annealing, quantum annealing and simulated quenching) and the strategies of the BBO algorithms (BOCS, FMQA and their variations) are discussed for further development of the BBO techniques.

A solution to bi/tri-level programming problems using particle swarm optimization

Combinatorial optimization problem arise typically in the form of a mixed-integer linear program $$ \max \left\{ {cx + dy:Ax + Dy \leqslant b,x \geqslant 0 and integer, y \geqslant 0} \right\}, $$ (MIP) where A is any m x n matrix of reals, b is a column vector with m real components and c and d are real row vectors of length n and p, respectively. If n = 0 then we have a linear program. If p = 0 then we have a pure integer program. The variables x that must be integer-valued are the integer variables of the problem, the variables y the real or flow variables. There are frequently explicit upper bounds on either the integer or real variables or both. In many applications the integer variables model yes/no decisions, i.e., they assume only the values of zero or one. In this case the problem (MIP) is a mixed zero-one or a zero-one linear program depending on p > or p = 0KeywordsExtreme PointIdeal FormulationMixed Integer Linear ProgramInteger SolutionLinear Programming RelaxationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Resource and energy efficiency are essential in process synthesis of chemical plants as they combine economic with ecological benefits. The two main targets of the process synthesis problem—mass and energy flux optimization—are typically split into two steps: single unit optimization and subsequent energy integration preventing the identification of the globally optimal solution. This article presents a single‐step procedure for resource‐efficient process synthesis through simultaneous heat and mass flux optimization called FluxMax approach (FMA), which is demonstrated for the production of hydrogen cyanide (HCN). The impact of simultaneous heat integration on the optimal process structure is demonstrated and two resource‐optimal processes for HCN production are identified consisting of a combination of different reactor and recycling strategies reducing total variable cost by 68%. For convex objective functions, the globally most resource‐efficient process is identified highlighting the potential of the FMA for site planning and retrofitting of existing plants.

Simulation and optimization of DMC synthesis process based on conventional and advanced exergy analyses

Signal timing optimization is one of the most important methods that improve the intersection serve level. The paper studies the signal optimization method that the phases have overlapped phase, the bi-level optimization method is used to solve the questions. Firstly, the critical phase is introduced in detail. Then the cycle optimization method TRRL and ARRB is described. Based on the cycle optimization method, the critical phase optimization method is given. Through an example, it is tested that intersection’s average delay is decreased by 21.37%, the average vehicle queue is decreased 10 pcu/c, the intersection capacity is increased 0.93% and the saturation of the intersection is reduce from 1.38 to 0.93. It is proved that the method given in this paper can make the signal time more effectively and the intersection has better serve level.

With the continuous promotion of the “dual carbon” idea, future power generation will rely heavily on renewable energy sources. As an effective utilization form of clean power sources, it is of positive significance to study the trading strategy of microgrids in the intelligent power distribution system under the influence of carbon quota. In this research, a bi-level optimization method is used to build a trading model of the distribution-side power market, with the upper-level planning aiming to reduce the cost of distribution system operators and the lower-level planning aiming to increase the revenue of microgrids. Secondly, the genetic algorithm and sequential quadratic planning algorithm are applied to the upper and bottom models to determine the optimal clearing strategy for the microgrid and the optimal scheduling scheme for the distribution system operator, respectively. Finally, a typical day is used as an example to analyze in detail the market trading strategy of a multi-microgrid intelligent distribution system under the influence of carbon quota, and the effectiveness of the method described in this paper is verified.

A bi-level optimization method for voltage control in distribution networks using batteries and smart inverters with high wind and photovoltaic penetrations

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is an NP-hard problem. In this paper, we tackle large-scale and combinatorial bi-level problems using GP hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the bi-level cloud pricing optimization problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization.

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.

We propose a mixed-integer nonlinear programming (MINLP) model for simultaneous utility and heat exchanger area targeting with variable stream conditions. The model represents the composite-curve-based area targeting method by constructing the hot and cold composite curves mathematically. We introduce a “dynamic” enthalpy grid onto which the stream inlet/outlet temperatures and enthalpies are mapped. By calculating the temperatures at each grid point and the stream heat duties at each interval, the utility consumption and heat exchanger areas are simultaneously optimized using an economic criterion. We discuss preprocessing methods tailored to aid the solution of the proposed MINLP model. The model is applied to two illustrative examples as well as an example where it is integrated with a process synthesis model.

Simultaneous process synthesis, heat, power, and water integration of thermochemical hybrid biomass, coal, and natural gas facilities