Integer Linear Programming Optimization Research Articles

Deep Reinforcement Learning-Based Smart Joint Control Scheme for On/Off Pumping Systems in Wastewater Treatment Plants

In this paper, we propose a deep reinforcement learning (DRL) based predictive control scheme for reducing the energy consumption and energy cost of pumping systems in wastewater treatment plants (WWTP), in which the pumps are operated in a binary mode, using on/off signals. As global energy consumption increases, the efficient operation of energy-intensive facilities has also become important. A WWTP in Busan, Republic of Korea is used as the target of this study. This WWTP is a large energy-consuming facility, and the pumping station accounts for a significant portion of the energy consumption of the WWTP. The framework of the proposed scheme consists of a deep neural network (DNN) model for forecasting wastewater inflow and a DRL agent for controlling the on/off signals of the pumping system, where proximal policy optimization (PPO) and deep Q-neural network (DQN) are employed as the DRL agents. To implement smart control with DRL, a reward function is designed to consider the energy consumption amount and electricity price information. In particular, new features and penalty factors for pump switching, which are essential for preventing pump wear, are also considered. The performance of our designed DRL agents is compared with those of WWTP experts and conventional approaches such as scheduling method and model predictive control (MPC), in which integer linear programming (ILP) optimization is employed. Results show that the designed agents outperform the other approaches in terms of compliance with operating rules and reducing energy costs.

Multi-Period Cost Optimization of Multi-Mode Carbon Capture and Storage Chains

A generally applicable multi-period and multi-mode CCS value chain cost model is developed to account for all individual costs and evaluate different options for CCS value chain implementation. The approach followed involves the configuration of a mixed integer linear programming optimization model, in which the objective function is to minimize the total cost of the CCS chain, considering constraints of mass balance, injection wells, intermediate storage sizing, flow and routing. The model functionality is illustrated through a case study based on a Norwegian CCS chain which is assessed for a planning horizon of 30 years, assigned in five-year long intervals. The model adopts the methodology developed previously by the authors for pipeline transport and geological storage of CO2 and introduces CO2 ship transport and intermediate storage at two design pressures (7 and 15 bar). It also includes a newly introduced CO2 conditioning step (compression and liquefaction) and a further injection mode, directly from ship. For the CCS chain analyzed, it is found that higher CO2 supply provides economy of scale gains, thus lower levelized cost. Pressure design for ship transportation plays a significant role on the cost of the chain with the trade-off between low conditioning costs and higher transport cost. In the Norwegian CCS case analyzed, individual pipelines from a single hub to each storage site are more economical than a single trunk-line with multiple connections to injection sites. Geological storage costs are largely driven by the availability and the operational costs of the individual sites.

Comparison of Batteries and Pumping Hydro with PaTs as energy storage technologies for a micro-hybrid generation system: Multi-objective optimization through a MILP algorithm

Small-scale hybrid energy systems are often composed by different power production technologies and adopted in mini-grids. In this work, a Mixed Integer Linear Programming optimization algorithm has been developed to compute the optimal scheduling of a micro-grid constituted by Internal Combustion Generators (ICGs) and a Storage System that can be either a conventional battery storage system or a Pumping Hydro energy Storage (PHES) based on Pump-as-Turbines. The algorithm computes the optimal energy generation scheduling of the micro-grid, minimizing a multi-objective fitness function constituted by the total costs of the energy system and the total CO2 and NOx emissions. In particular, the emissions are modelled with varying trends depending on the ICG load and not with constant values, which represents a simplification that is often adopted but that can induce misleading results. Furthermore, the algorithm takes into account all the physical constraints related to the generators and the storage system, such as maximum and minimum power generation, ramp-up and ramp-down limits and minimum up and down-time. The two energy storage technologies are compared and results show that a management strategy based on this algorithm can reduce significantly the total emissions of the system.

Demand Management for Optimized Energy Usage and Consumer Comfort Using Sequential Optimization.

The Energy-efficiency of demand management technologies and customer’s experience have emerged as important issues as consumers began to heavily adopt these technologies. In this context, where the electrical load imposed on the smart grid by residential users needs to be optimized, it can be better managed when customer’s comfort parameters are used, such as thermal comfort and preferred appliance usage time interval. In this paper a multi-layer architecture is proposed that uses a multi-objective optimization model at the energy consumption level to take consumer comfort and experience into consideration. The paper shows how our proposed Clustered Sequential Management (CSM) approach could improve consumer comfort via appliance use scheduling. To quantify thermal comfort, we use thermodynamic solutions for a Heating Ventilation and Air Conditioner (HVAC) system and then apply our scheduling model to find the best time slot for such thermal loads, linking consumer experience to power consumption. In addition to thermal loads, we also include non-thermal loads in the cost minimization and the enhanced consumer experience. In this hierarchal algorithm, we classified appliances by their load profile including degrees of freedom for consumer appliance prioritization. Finally, we scheduled consumption within a Time of Use (ToU) pricing model. In this model, we used Mixed Integer Linear Programming (MILP) and Linear Programming (LP) optimization for different categories with different constraints for various loads. We eliminate the customer’s inconvenience on thermal load considering ASHRAE standard, increase the satisfaction on EV optimal chagrining constrained by minimum cost and achieve the preferred usage time for the non-interruptible deferrable loads. The results show that our model is typically able to achieve cost minimization almost equal to 13% and Peak-to-Average Ratios (PAR) reduction with almost 45%.

Role of green data center in human resources development model

Human resources are now a very important factor in the development of various enterprises and even countries, and how to master and manage human resources has always been a problem. Nowadays, the development of science and technology has made the processing of various data simple, and green data processing centers that save energy have also been established. This paper is to study the role of green data center in terms of human resources development model, by using mixed integer linear programming optimization mathematical model of the existing data center, reduce data center energy consumption NRE while establishing a new green data center, and the data center conducts related research on the information search of human resources and its auxiliary role on how to efficiently hire and train employees. The research results show that the green data center can play a great role in the development of human resources. Compared with relying solely on human resource managers, the use of green data centers to manage human resources has increased the decision-making ability of corporate personnel by 12 %, and personnel efficiency has increased by about 10 %, basically realizing comprehensive and effective management of human resources.

Energy-Efficient Heterogeneous Networking for Electric Vehicles Networks in Smart Future Cities

Electric vehicles networks have become hot topics in research and industry and played an important role in smart future cities. However, high energy consumption is a significantly challenge for these applications. This article proposes a electric vehicles cloud computing framework to perform energy-efficient heterogeneous networking for electric vehicles network in smart future cities. The software-defined networking ideas is used to enable different devices including electric vehicles to access the cloud computing network for electric vehicles connected. The edge computing is exploited to run quick computing and communication for these application. Then an energy-efficient heterogeneous networking method is presented to overcome high energy consumption. The mixed integer linear programming optimization model and two heuristic models are proposed to perform energy-efficient networking. An networking algorithm is proposed to achieve highly energy-efficient networking for electric vehicles network. The detailed simulation experiments are conducted to validate our approach. Simulation results illustrate that the proposed method is efficient and feasible.

Hybrid Energy Network Management: Simulation and Optimisation of Large Scale PV Coupled with Hydrogen Generation

The power production of electrical Renewable Energy Sources (RES), mainly PV and wind energy, is affected by their primary source of energy: solar radiation value or wind strength. Electrical networks with a large share of these sources must manage temporal imbalances of supply and demand. Hybrid Energy Networks (HEN) can mitigate the effects of this unbalancing by providing a connection between the electricity grid and and other energy vectors such as heat, gas or hydrogen. These couplings can activate synergies among networks that, all together, increase the share of renewable sources helping a decarbonisation of the energy sector. As the energy system becomes more and more complex, the need for simulation and optimisation tools increases. Mathematical optimisation can be used to look for a management strategy maximising a specific target, for instance economical, i.e. the minimum management cost, or environmental as the best exploitation or RES. The present work presents a Mixed Integer Linear Programming (MILP) optimisation procedure that looks for the minimum running cost of a system made up by a large-scale PV plant where hydrogen production, storage and conversion to electricity is present. In addition, a connection to a natural gas grid where hydrogen can be sold is considered. Different running strategies are studied and analysed as functions of electricity prices and other forms of electrical energy exploitation.

Designing efficient communication infrastructure in post-disaster situations with limited availability of network resources

In the aftermath of a large-scale disaster, such as an earthquake, existing telecommunication (e.g., cellular towers) and other public infrastructures (e.g., power lines, roads, etc.) are often severely damaged. This prevents the seamless exchange of situational awareness and rescue/relief based information between the volunteers, shelter points, and the coordination center. A temporary communication infrastructure utilizing smartphones, communication towers, drones, smart badges, etc., commonly referred to as network resources, can be formed, which promises to bridge the communication gap between various stakeholders in the disaster area and enable timely information exchange between them. However, the efficacy of such networks is often challenged by the limited availability of network resources in the disaster area. To address this issue, in this paper, we propose to design, develop, and test a novel strategy that intelligently deploys the limited network resources in the disaster area, and thus, creates an efficient temporary communication infrastructure in such resource-constrained post-disaster situations. We formulate the network resource deployment (ResDep) problem as an integer linear programming optimization problem and show that it is NP-Hard. Next, we propose a near-optimal polynomial-time heuristic solution for solving it. Our extensive simulation study on top of ONE simulator and proof-of-concept pilot deployment study, both based on our university, National Institute of Technology — Durgapur, India, reveal that the proposed heuristic performs nearly as well as that of the optimal solution (computed using Gurobi optimizer), and outperforms its variant and baseline approaches when compared in terms of end-to-end network latency and message delivery.

Assessment of Bisimulation Non-Interference in Discrete Event Systems Modelled With Bounded Petri Nets

Non-interference in discrete event systems deals with the possibility by an intruder to infer the occurrences of private and non observable events, the so called high-level ones, by interacting with the system at a user level, i.e., by observing the occurrence of the so called low-level ones. When bisimulation non-interference is considered, the security objective is not only to avoid the detection of high-level event occurrences, but also to avoid the detection of their non occurrences; i.e., the secret includes also the non occurrences of some events. This letter deals with such a more restrictive security property in the framework of discrete event systems modelled as Petri nets. A necessary and sufficient condition is given to assess bisimulation non-interference in bounded Petri nets. Such a condition requires the solution of integer linear programming optimization problems, whose solution can be used also to statically enforce bisimulation non-interference when this condition is not satisfied by the original system.

Identifying Drug Sensitivity Subnetworks with NETPHIX

SummaryPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective function (OF) differences below 1.5% for 77% of the Microgrids. The other cases show OF differences between 6% and 13%, which can be reduced to 1.5% or less by applying a two-stage hybrid approach that designs the Microgrid based on down-sampled data and then performs a full-scale dispatch algorithm. This two stage approach results in 20–99% run-time savings.

Optimal sizing of a Hybrid Renewable Energy System: Importance of data selection with highly variable renewable energy sources

The replacement of fossil fuels for producing energy with renewable sources is crucial to limit the climate change effects. However, the unpredictable nature of renewables, like sun and wind, complicates their integration within the power systems. This problem can be faced with the introduction of Hybrid Renewable Energy Systems (HRESs) where several energy sources can be incorporated. A key aspect is the assessment of the HRES configuration, which is fundamental to obtain a feasible system from both technical and economic points of view. In this paper, a novel Mixed Integer Linear Programming (MILP) optimization algorithm has been developed to design a tool capable of assessing the optimal sizing of a HRES. The algorithm has been applied to a real case study of a mountain hut located in South-Tyrol (Italy) with a hybrid system composed by solar, wind and diesel generators together with a battery storage. The algorithm compares several scenarios providing the optimal configurations of the HRES, which are characterized by different costs and energy deficits. This tool helps engineers to identify the best trade-off between costs and energy deficits in the planning phase of a HRES, still granting the demand of the users as well as the constraints.

Reallocation of unoccupied beds among requesting wards

Disruptions can cause demand fluctuation, thus overcrowding at hospital wards, and can make the waiting list of patients longer. Bed management at hospitals is one of the solutions to deal with overcrowding. In addition, Resilience Engineering (RE) is an approach that can help organizations to bounce back to their desired performance state after a disruption. In this paper, the concept of RE has been used to improve the bed management of hospitals during and after disruption. More precisely, bed sharing among hospital wards has been introduced as a collaboration strategy and its impact on the length of patients’ waiting list as the major performance index is investigated. Relationship priority between different wards, patients’ gender, patients’ length of stay and the number of rooms in every ward are the major factors considered in our modeling. A mixed integer linear programming optimization model with the objective of minimizing the patients’ waiting time in a hospital has been proposed for the real-world problems. The main contribution of the present paper is proposing a resiliency-based modeling of bed management in hospitals. Due to the complexity and making the proposed model applicable to the real world problems, a simulated annealing algorithm is used to solve the model and a new procedure for creating initial solution is presented. The results show that applying resilience strategy has a considerable impact on improving the hospital’s performance index.

A Mixed Integer Linear Programming Based Load Shedding Technique for Improving the Sustainability of Islanded Distribution Systems

In recent years significant changes in climate have pivoted the distribution system towards renewable energy, particularly through distributed generators (DGs). Although DGs offer many benefits to the distribution system, their integration affects the stability of the system, which could lead to blackout when the grid is disconnected. The system frequency will drop drastically if DG generation capacity is less than the total load demand in the network. In order to sustain the system stability, under-frequency load shedding (UFLS) is inevitable. The common approach of load shedding sheds random loads until the system’s frequency is recovered. Random and sequential selection results in excessive load shedding, which in turn causes frequency overshoot. In this regard, this paper proposes an efficient load shedding technique for islanded distribution systems. This technique utilizes a voltage stability index to rank the unstable loads for load shedding. In the proposed method, the power imbalance is computed using the swing equation incorporating frequency value. Mixed integer linear programming (MILP) optimization produces optimal load shedding strategy based on the priority of the loads (i.e., non-critical, semi-critical, and critical) and the load ranking from the voltage stability index of loads. The effectiveness of the proposed scheme is tested on two test systems, i.e., a 28-bus system that is a part of the Malaysian distribution network and the IEEE 69-bus system, using PSCAD/EMTDC. Results obtained prove the effectiveness of the proposed technique in quickly stabilizing the system’s frequency without frequency overshoot by disconnecting unstable non-critical loads on priority. Furthermore, results show that the proposed technique is superior to other adaptive techniques because it increases the sustainability by reducing the load shed amount and avoiding overshoot in system frequency.

A benders-decomposition-based transient-stability-constrained unit scheduling model utilizing cutset energy function method

Rapid growth of load demand and concurrently system inertia deterioration put power systems at risk of transient instability. Therefore, operation of power systems as bulk complex systems must be secured against transient instability not only in hourly optimal power flow studies, but also in daily unit scheduling. To address this challenge, in this paper, secure and economic operation of power system is ensured through a Transient Stability-Constrained Unit Commitment (TSCUC) model employing Benders Decomposition (BD) technique. The proposed TSCUC model consists of one master problem determining committed units and two distinct sub-problems verifying the steady state impacts of single outages, and transient stability criteria, respectively. This paper proposes a state transition formulation for master problem as a mixed integer linear programming optimization problem preserving both compactness and tightness of the problem. A structure-preserving transient stability assessment approach called cutset energy function method is developed to assess the transient stability of the system for each configuration of the committed units, under a set of probable contingencies. Several case studies on a dynamic test system are demonstrated to validate the efficacy of the proposed TSCUC algorithm. Finally, the proposed method performance is compared with the state-of-the-art methods.

Environmental and economic optima of solar home systems design: A combined LCA and LCC approach

This paper compares the economic and environmental optimal design of Solar Home Systems (SHSs) and explores the role of economic incentives (such as tariffs and technology costs) in approximating the two optima. To achieve that, we present a methodology for the environmental and economic evaluation of grid-connected SHSs: user-scale electric systems involving a photovoltaic (PV) power system and a battery energy storage system. The proposed methodology is based on a mixed integer linear programming (MILP) optimization, life cycle assessment and life cycle costing. This methodological framework is applied to a case study involving a typical SHS installation in Italy. The results of the environmental optimal design brought to the evaluation of a 3.25 kW PV assisted by 8.66 kWh of nickel cobalt manganese batteries, whereas the costs of the SHS are minimized by a small PV system (less than 1 kW). Results underline that the environmental optimal configurations rely on battery technologies, which entails a significant cost compared to the grid connection. In contrast, the economic optimal design solutions is less impactful than the grid mix both from an environmental and economic points of view. Thanks to a reduction of batteries and PV costs, the environmental impact of the economic optimal design is expected to decrease in the future.

Resource Allocation Strategy of Edge Systems Based on Task Priority and an Optimal Integer Linear Programming Algorithm

With the emergence of increasingly computing-intensive and delay-sensitive tasks, the processing of computing tasks on cloud servers cannot meet the current needs any longer. The emergence of mobile edge computing (MEC) technology and the popularity of 5G applications can solve these demands. Offloading tasks to the MEC server reduces the energy consumption of local devices, and also has a lower latency than offloading to the cloud server. In this paper, an MEC–edge cloud server collaborative system model with energy harvesting technology is designed to minimize the processing delay of computing tasks by allocating computing resources. We propose an optimal integer linear programming (OILP) algorithm with two steps. Firstly, we propose a Lyapunov stability optimization algorithm based on task priority. With the constraints of local mobile device power stability, the divide-and-conquer idea is used for solving the target values of the processing tasks locally, and the MEC and edge cloud servers separately. Therefore, the objective problem is transformed into an integer linear programming problem, and then an integer linear programming algorithm based on CPU utilization optimization is proposed to obtain a resource allocation scheme. Simulation results show that the proposed OILP algorithm can further reduce the delay, improve the CPU’s utilization of the MEC server, and reduce the number of the tasks that cannot be processed under the condition of the energy stability of the local device.

Comparison of two hybrid algorithms on incorporated aircraft routing and crew pairing problems

<span>In airline operations planning, a sequential method is traditionally used in airline system. In airline systems, minimizing the costs is important as they want to get the highest profits. The aircraft routing problem is solved first, and then pursued by crew pairing problem. The solutions are suboptimal in some cases, so we incorporate aircraft routing and crew pairing problems into one mathematical model to get an exact solution. Before we solve the integrated aircraft routing and crew pairing problem, we need to get the aircraft routes (AR) and crew pairs (CP). In this study, we suggested using genetic algorithm (GA) to develop a set of AR and CP. By using the generated AR and CP, we tackle the integrated aircraft and crew pairing problems using two suggested techniques, Integer Linear Programming (ILP) and Particle Swarm Optimization (PSO). Computational results show that GA's executed of AR and CP and then solved by ILP obtained the greatest results among all the methods suggested.</span>

Waste-to-Resource value chain optimisation: Combining spatial, chemical and technoeconomic aspects

Due to complex composition of carbohydrates, lipid, protein, cellulose, hemicellulose and lignin, wastewater (WW) and organic fraction municipal solid waste (OFMSW) represent nutrient and carbon rich resources. Conventionally, value chains in the waste sector have considered OFMSW and WW as unwanted by-products as opposed to potential valuable resources. Full exploitation of these resources calls for a value chain transformation towards proactive resource recovery. This study focuses on the waste supply chain optimisation to recover value added products from OFMSW.The research leads to a systems-modelling approach, which integrates spatial data analyses, mathematical mixed integer linear programming (MILP) optimisation and technology performance evaluation to inform the design of waste-to-resource value chains. A UK based study on OFMSW is presented to demonstrate the efficacy of the approach. The study captures variation in OFMSW quantity and composition, incorporating over 600 existing anaerobic digestion (AD) operational plants in the UK, while potential sites for new waste-recovery facilities are identified, accounting for transportation and logistics, using a GIS-based analysis. Key outcomes are analysed (technology type, size, location, logistical connections), placing emphasis on the need to consider the value of the resource recovery potential over the lifetime of an AD or thermochemical treatment facility in the design process. Such an approach offers a promising pathway for tackling the open challenges currently hindering the waste-to-resource transformation.

A Short-Term Planning Framework for the Operation of Tanker-Based Water Distribution Systems in Urban Areas

Tanker-based distribution systems have been prevalent in developing countries to supply clean and pure water in different regions. To efficiently operate such tanker service systems, a large fleet of tanker trucks are required to transport water among several water sources, water treatment plants and consumers spanning across the regions. This requires tighter coordination between water suppliers, treatment plant operations, and user groups to use available water resources in a sustainable manner, along with the assurance of water quality and timely delivery. This paper proposes a novel formulation to assist decision-making for optimizing tanker-based water distribution systems and treatment operations, with an overall objective of minimizing the total operating cost such that all of the constraints related to the water demand, supply operations, and environmental and social aspects are honored while supplying water to a maximum number of users. The problem is formulated and solved as a mixed integer linear programming (MILP) optimization framework and captures all of the nuances related to (i) water availability limitations and quality constraints from different sources, (ii) maintaining water quality as it transports via tankers, (iii) water demands for various end-use purposes, and (iv) transportation across a water supply chain. The proposed novel framework is applied to a realistic urban model to find the optimal tanker delivery schedule, ensuring appropriate treatment and timely delivery of water. The results of the case study conducted on a representative-scale problem also elucidate several aspects of treatment plant operation and consumer demand fulfillment for the efficient planning and management of tanker-based water distribution systems.