Improving Power Network Resilience Against Threats

This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.

Predictive route choice control of destination coded vehicles with mixed integer linear programming optimization

This paper aims to develop a suitable restoration method by a joint usage of an Expert system (ES) and a Mathematical Programming (MP) approach. Under this devise, the optimal target system for storation is formulated as an MP problem and solved efficiently by the decomposition-coordination strategy; that is a whole problem is decomposed into subproblems according to the generic knowledge of experts. The introduction of a new concept ?>restorative operation cost ?> that reflects the various restoration strategies has decreased the number of rules in the knowledge-base to one third that of typical existing ES's. The validity and effectiveness of the combined method have been demonstrated by applying it to a practical size restoration problem.

Mitigation of cyber-physical attacks in multi-area automatic generation control

As several mixed-integer linear programming (MILP) optimization models have been developed in the past few years for design, planning, and scheduling of chemical processes, questions arise about their sensitivity to uncertainties in the problem data and modeling assumptions. We present a study of the effects of time horizon discretization and inaccurate data on the quality of the MILP solution. The results provide information on how detailed discretizations and how accurate forecasts are required in order to obtain good-quality solutions with the MILP model. The studies indicate that the LP relaxation gap of the MILP reduces as the number of time periods of the planning model is increased. Another interesting finding is that, for all the problems solved, uncertainty in prices and demands does not seem to have any major impact on the quality of the solution of the MILP model as long as plan revision is allowed through adjustment of production levels and amounts of purchases and sales. We also develop an efficient solution method for the planning problem and demonstrate its superiority to a popular MILP package

The demand for hydrogen in refineries is growing due to its importance as a sulfur capture element. Therefore, hydrogen management is critical for fulfilling demands as efficiently as possible. Through mathematical modeling, hydrogen network management can be better performed. Cost-efficient Mixed-Integer Linear Programming (MILP) and Mixed-Integer Nonlinear Programming (MINLP) optimization models for (re)designing were proposed and implemented in GAMS with two case studies. Linear programming has the limitation of no stream mixing allowed; therefore, to overcome this limitation, an algorithm-based procedure called the Virtual Compressor Approach was proposed. Based on the MILP optimal solution obtained, the streams and compressors were merged. As a result, the number of compressors was reduced, along with the inherent investment costs. An operational cost reduction of more than 28% (example 1) and 26% (example 2) was obtained with a linear model. The optimal MILP solution after rearranging compressors was then provided as a good starting point to the MINLP. The operating costs were decreased by more than 31% (example 1) and 32% (example 2). Most of the cost reduction was obtained only with the usage of the MILP model. Besides, a higher level of cost reduction was only obtained when the linear model was used as the starting point.

In this paper we explore the possibility of deriving consensus rankings by solving consensus optimization problems, characterizing consensus rankings as suitable complete order relations minimizing the average Kemeny-Snell distance to the individual rankings. This optimization problem can be expressed as a binary programming (BP) problem which can typically be solved reasonably efficiently. The underlying theory is discussed in Sect. 1. Applications of the proposed method given in Sect. 2 include a comparison to other mathematical programming (MP) approaches using the data set of Tse [9] and establishing a consensus ranking of marketing journals identified by domain experts from a subset of the Harzing journal quality list [2]. In Sect. 3 we discuss computational details and present the results of a benchmark experiment comparing the performance of the commercial solver CPLEX to three open source mixed integer linear programming (MILP) solvers

At substations of district heating systems, water is heated and transferred by pumps through piping networks to the buildings of each local heating system. When they were designed, the operational parameters and system characteris tics of the local heating system were selected to provide thermal comfort in all the buildings served by the local heating system. After several years of opera tion, however, these parameters and characteristics change and, as a result, thermal comfort may be expected to deteriorate. This study investigates one strategy to mitigate this problem; that of retrofitting the local heating system with additional circulation pumps to provide greater system control and, thereby, improved thermal comfort. Using a steady-state, bottom-up ap proach, heat transfer in the local heating system is described by a system of equations that are then linearized. Mixed integer linear programming is then applied to these equations, implemented in the program PUMP, and used to optimize system characteristics. The results indicate that pump-placement optimization strategy is more effective in overcoming changes in hydraulic resistance than variations in radiator and building heat transmittance result ing from aging of the local heating system.

The ever-increasing development in the world causes load demand to increase exponentially each year. The current transmission and distribution systems cannot meet this load demand. Moreover, the peak demand occurs only for a short duration. However, it stresses the power system and results in system losses and damages, which can be avoided by load shedding at the cost of customer discomfort and economic losses to utilities. Energy Storage System (ESS) is a feasible option to counter this problem. In this paper, an optimal mixed integer linear programming (MILP) based computational energy arbitrage model of ESS is proposed for efficiently scheduling its charging and discharging power to avoid overloading of distribution feeder. The proposed optimization model ensures satisfaction of feeder thermal limit constraints. The case study of distribution feeder with ESS deployment is presented for proving the effectiveness of the proposed model. The results clearly demonstrate the ESS economical operation to reduce feeder peak load and maximize ESS profit.

This work proposes a hybrid scheme that combines a simulation model and a mathematical programming model for designing logistic networks for co-firing biomass, specifically switchgrass, in conventional coal-fired power plants. The advantages of co-firing biomass include: (1) the creation of green jobs; (2) the efficient use of current power plant infrastructure; (3) fostering the penetration of renewable energy into power networks; and, (4) the reduction of greenhouse gas (GHG) emissions. The novelty of this work lies in the inclusion of (1) the inherent variability of biomass supply at the parcel level, and (2) the effects of climate change on future biomass supply when designing a feedstock logistic network. The design optimization is conducted at the farm/parcel level (most, if not all, previous works have used county level average data) and integrates the crop growth predictions employing United States Department of Agriculture’s (USDA’s) Agricultural Land Management with Numerical Assessment Criteria (ALMANAC) simulation model; the output of the simulations is input into the mixed integer linear programming (MILP) hub-and-spoke model to minimize the overall cost of the logistic network. Specifically, the MILP-based model selects the parcels and depot locations as well as biomass transportation flows by taking into consideration different types of soil, land cover characteristics, and predicted yields, which account for both historical and forecasted weather data. The hybrid methodology was tested by solving realistic situations, which considered varying weather conditions. The gross results indicate that the optimized logistic network enabled meeting a 20% biomass co-firing rate demand, which reduced 1,158,867 Mg per year in GHG emissions by co-firing with biomass.

Rapid online planning of train operation strategy in response to emergencies is a critical means of ensuring the safe and efficient operation for high-speed railways. However, it is difficult to achieve punctual, comfortable, and energy-saving operation under emergency scenarios due to the properties of high speeds, long inter-station distances, and the presence of long continuous gradients. This paper proposes an online planning approach of train operation curve based on dynamic programmingdeep Q network (DP-DQN). Firstly, the dynamic programming (DP) algorithm is used to optimize some train operation curves, and the state quadruples it contains are put into the memory buffer and incorporated into the reward function to guide and accelerate the network training. Then, the action mask from reinforcement learning is used to filter out the actions that cause the train speed to exceed the Automatic Train Protection speed, enabling fast and safe online planning of the speed curve under emergencies. Based on the actual data from a high-speed railway line, the proposed approach is evaluated in offline optimization. Experimental results show that, compared with mixed integer linear programming (MILP) and particle swarm optimization (PSO) algorithms, the DP-DQN approach yields small errors in train operation punctuality and stopping accuracy, better passenger comfort, and a reduction of energy consumption by 2.99% and 12.30%, respectively. Additionally, emergency scenarios involving temporary speed limit, traction/braking failure and emergency stop are simulated and analyzed. While DP-DQN results in slightly higher energy consumption than MILP in some scenarios, it significantly reduces train delays, ensures stopping accuracy, and completes online strategy planning within one second, which meets the real-time requirements of online planning.

Assessing solution quality and computational performance in the hydro unit commitment problem considering different mathematical programming approaches

임베디드 기기는 시퀀스 제어 기능과 수치연산 기능을 활용하여 제어 프로그램에 따라 산업현장의 기기 등 다양한 자동화 시스템에 활용된다. 현재 임베디드 기기는 기업의 산업현장, 원전, 대중교통 같은 국가기반시설에서 제어 시스템으로 활용되고 있다. 따라서 임베디드 기기를 대상으로 하는 공격은 큰 경제적 손실과 사회적 손실을 야기할 수 있다. 임베디드 기기를 대상으로 하는 공격은 대부분 데이터, 코드 변조로서 제어 프로그램을 대상으로 이루어진다. 산업 자동화 임베디드 기기의 제어 프로 그램은 일반적인 프로그래밍 언어와 달리 회로 구조를 표현하기 위하여 설계되었고, 대부분의 산업 자동화 제어 프로그램은 그래픽 기반 언어인 LAD로 설계되어있어 정적분석이 용이하지 않다. 이러한 특징으로 인하여 산업 자동화 제어 프로그램에 대한 취약점 분석 및 보안 관련 연구는 정형 검증, 실시간 모니터링 수준에 그친다. 또한 사전에 취약점을 탐지하고 공격에 대한 대비가 가능한 산업 자동화 제어 프로그램 정적분석 연구는 매우 저조한 실정이다. 따라서 본 연구에서는 산업 자동화 임베디드 프로그램에 대한 정적분석 효율성 증대를 위하여 회로 구조를 표현하기 위해 설계된 산업 자동화 제어 프로그램을 논리식으로 표현하기 위한 방법을 제시한다. 또한 다양한 제조사의 산업 자동화 제어 프로그램을 통합적으로 분석하기 위하여 LLVM IR을 활용한 중간어 변환 기술을 제안한다. LLVM IR을 활용함으로서 동적 분석에 대한 통합분석이 가능하다. 본연구에서는 해당 방법에 대한 검증을 위하여 S 社의 제어 프로그램을 대상으로 하여 논리식 형태의 중간어로 변환하는 프로그 램의 시제품을 개발하였다.Utilizing sequence control and numerical computing, embedded devices are used in a variety of automated systems, including those at industrial sites, in accordance with their control program. Since embedded devices are used as a control system in corporate industrial complexes, nuclear power plants and public transport infrastructure nowadays, deliberate attacks on them can cause significant economic and social damages. Most attacks aimed at embedded devices are data-coded, code-modulated, and control-programmed. The control programs for industry-automated embedded devices are designed to represent circuit structures, unlike common programming languages, and most industrial automation control programs are designed with a graphical language, LAD, which is difficult to process static analysis. Because of these characteristics, the vulnerability analysis and security related studies for industry automation control programs have only progressed up to the formal verification, real-time monitoring levels. Furthermore, the static analysis of industrial automation control programs, which can detect vulnerabilities in advance and prepare for attacks, stays poorly researched. Therefore, this study suggests a method to present a discussion on an industry automation control program designed to represent the circuit structure to increase the efficiency of static analysis of embedded industrial automation programs. It also proposes a medium term translation technology exploiting LLVM IR to comprehensively analyze the industrial automation control programs of various manufacturers. By using LLVM IR, it is possible to perform integrated analysis on dynamic analysis. In this study, a prototype program that converts to a logical expression type of medium language was developed with regards to the S company s control program in order to verify our method.

Redundancy and hardening of components in transmission systems are practices to improve system resilience against possible adverse consequences caused by natural hazards. However, power systems planners do not usually recognize the natural hazards, so-called high-impact low-probability (HILP) events, within network investment decisions. The situation is exacerbated by an increase in the frequency and severity of power system blackouts due to HILP-type events. In this context, this paper proposes a planning problem for investment in transmission lines and hardening of substations to improve the resilience of power system considering the outage scenarios originated by earthquakes. The problem is formulated as a mixed-integer linear programming (MILP) approach. Furthermore, a probabilistic methodology is used to simulate the hazard and its impacts on the power system. Our proposal is applied on the Ecuadorian transmission system. The results demonstrate that hardening substations and investment in lines can significantly contribute towards enhancing resilience to high impact exogenous natural hazards.

The global climate protection policy aimed at achieving a zero greenhouse gas emissions target has led to the fast incorporation of large-scale photovoltaics into the power network. The conventional AC grid was not modeled to be incorporated with large-scale non-synchronous inverter-based energy resources (IBR). Incorporating inertia-free IBR into the grid leads to technical issues such as the degradation of system strength and inertia, therefore affecting the safety and reliability of the electrical power system. This research introduced a new solution to incorporate a flywheel in the rotor of a synchronous machine to improve the dynamic inertia control during a system disruption and to maintain the constancy of the system. The objective of this work is to enhance large-scale photovoltaic systems in such a way that they can avoid failures during a fault. A model of transient constancy with two synchronous generators and a LSPV is established in PowerWorld modeling software. A line-to-ground and three-phase fault are simulated in a system with up to 50% IBR penetration. The outcomes showed that the power network was able to ride through faults (RTFs) and that the stability of frequency and voltage are enhanced because of a flywheel that improved grid inertia and strength.