Integer Linear Programming Optimization Research Articles

Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project.

A scenario-based sustainable dual-channel closed-loop supply chain design with pickup and delivery considering social conditions in a natural disaster under uncertainty: a real-life case study.

In the context of the COVID-19 pandemic, utilizing the potential of online markets to sell products has become increasingly important for creating competitive advantages and ensuring the growth and survival of businesses. The pandemic has disrupted traditional business practices, and with social distancing measures in place, consumers have turned to online channels to meet their needs. As a result, businesses that have adapted quickly to online markets have been able to maintain their customer base and revenue streams. Thus, considering the potential of online markets is of utmost importance in the current pandemic situation. In this regard, the present research aims to provide a practical framework for creating a green and sustainable closed-loop supply chain network (SCLSCN), including the integration of online markets, to assist managers in making decisions that support economic, environmental, and social goals. Accordingly, a multi-objective mixed integer linear programming (MOMILP) optimization model was designed under uncertain demand and disruption caused by natural disasters in Iran's home appliance industry. The study also considered changes in the capacities of online and marketplace sales channels, revealing a significant reduction in costs at each stage. The results show that the increase in demand has a direct impact on the production level, warehousing, and transportation costs, leading to social impacts on the model. However, the current system cannot handle an increase in demand of more than 20%, requiring managers to make decisions to increase production capacity or build new factories. Thus, the study highlights the importance of considering online markets as a means to adapt to disruptions caused by the pandemic and maintain a competitive edge.

Multi-Period Optimisation of District-Scale Building Integrated Photovoltaic Deployment

The deployment of solar energy technologies such as building integrated photovoltaics (BIPVs) is one of many ways to mitigate present greenhouse gas emissions and abate GHG emissions in future urban development. The widespread deployment of BIPVs is therefore ideal, but deployment must be met with an understanding of the life cycle carbon associated with the technologies manufacturing and their availability as a finite resource. In this paper we describe deployment schedules over the next several decades for BIPVs in a redeveloping urban quarter in Zürich, Switzerland, known as Altstetten. Altstetten has near term goals for carbon neutral buildings that must be met through energy retrofits, higher building standards, and renewable energy. In solving how best to deploy BIPV systems to reduce the life cycle emissions of the buildings in Altstetten, we develop a multi period deployment scheme from a Mixed Integer Linear Programming optimization model that determines the optimal PV deployment within a larger urban multi-energy system. This approach intermingles spatially resolved optimisations with temporally resolved projections of how to deploy the BIPV systems. We analyse results of several optimal solutions sets to investigate the role urban morphology and BIPV embodied emissions play in BIPV utility. We find that different planning strategies (e.g. reduce cost before carbon emissions) play a role in the quantity and rate at which BIPV could be expected to be deployed and that a strategy of minimizing carbon before cost results in less required BIPV deployment than if cost is of the prime concern.

Model predictive control of solar-coupled innovative heat pump: a comparison of economic and environmental optimizationsin Latvia.

Background: Heating and cooling in buildings represents a significant amount of the energy demand in the EU, but the market penetration of renewable solutions is still marginal. The SunHorizon project aims at proving the viability and benefits of innovative coupling between heat pumps and various advanced solar panels. Methods: This study focuses on the optimal operation strategies of a technological package located in Latvia, and composed of hybrid photovoltaic thermal (PVT) panels, a gas driven heat pump and a hot water storage tank. In this work, a model predictive control is developed, based on mixed integer linear programming (MILP) optimization. This model uses innovative elements compared to traditional model predictive control (MPC), with environmental indicators for the Latvian electricity grid accounting for imports, co-simulation with TRNSYS using the transmission control protocol (TCP) and modelling of long-term storage for long and short-term decisions. The usual minimization of costs is compared to two new optimization approaches, which aims to minimize greenhouse gas (GHG) emissions and maximizing renewable use and self-consumption. Results and conclusions: The results of the optimization of costs and GHG emissions show that gains can be found within the variations in time series related to the electricity grid, but the overall operation strategies remain similar. Optimization of renewable share and self-consumption is another path for control strategy, but with less economic and environmental performance.

The economic impact of location on a solar farm co-located with energy storage

Deploying energy storage (ES), alongside renewable generation, can help to decarbonise electricity grids. A key aspect of deploying these is choosing a suitable location, which is both geographically feasible and economical. Previous studies identify locations with suitable geographies; here we focus on the economic impact of location. We explore how the maximised profits, determined using a mixed integer linear programming (MILP) optimisation model, of a solar farm co-located with ES vary in different regions around Great Britain (GB) as a case study. We perform a cost–benefit analysis from the point of view of a distribution-connected solar farm owner. Real solar generation data is used, along with a weather model, to accurately represent forecast and actual output. For solar farms without ES profits are higher in locations with greater solar irradiance. However for sites with ES we find greater profit variation, primarily due to different distribution charges. For the majority of GB, ES does not add sufficient value to offset its high upfront costs and is not worth adding to solar sites. Additionally, it is found to be uneconomical to add ES to most existing solar farms, despite many studies highlighting the grid benefits this would bring. We recommend that distribution network operator and market pricing better reflects the value which ES can bring to the electricity system economical to add to solar sites. To encourage increased co-location distribution operators should offer greater a differential between non-intermittent generation and intermittent generation payments, in particular at times of high system demand.

Key-count differential-based proactive key relay algorithm for scalable quantum-secured networking

By overcoming the distance limit in quantum key distribution (QKD) technology, key relaying over trusted nodes is regarded as an essential function for practical quantum-secured networking. The key relay function consumes a series of costly quantum key resources to establish an end-to-end key in QKD networks (QKDNs); thus, an efficient key relay algorithm is required. To investigate this problem, we developed an integer linear programming (ILP) formulation that maximizes a balanced summation between max-min fairness and efficiency in the key relay. Inspired by the ILP model, this study developed a key-count differential-based proactive key relay (DPKR) heuristic algorithm to provide a scalable solution for key relays in QKDNs. The proposed DPKR algorithm iteratively selects a set of nodes by considering the key-count differential between nodes and establishes an end-to-end key by relaying it over the selected nodes. Owing to the proactive key relay model, an effective key relay route in the key management layer in the QKDN can be calculated solely by the key-count status in the key management layer. The proposed DPKR algorithm reduces manifold order-of-magnitude in the algorithm computation time at the cost of an acceptable loss in the average and minimum numbers of keys from those of the ILP optimization model. The computation time evaluation clearly manifests a scalability of the DPKR algorithm in quantum-secured networking.

Power generation expansion planning approach considering carbon emission constraints

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality. The power sector accounts for approximately 40% of China’s total CO2 emissions. Accordingly, collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic, environmental, and technological concerns as carbon emissions is necessary. This paper proposes a collaborative mixed- integer linear programming optimization approach for GEP. This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies, flexible generation, and resource limitations constraints. This research further analyzes the advantages and disadvantages of current GEP techniques. Results show that the main determinants of new investment decisions are carbon emissions, reserve margins, resource availability, fuel consumption, and fuel price. The proposed optimization method is simulated and validated based on China’s power system data. Finally, this study provides policy recommendations on the flexible management of traditional power sources, the market-oriented mechanism of new energy sources, and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

Efficient skeleton curve-guided five-axis sweep scanning based on optimal traversing of multiple connected skeleton curves

The five-axis sweep scanning approach is an emerging surface inspection technology which could tremendously boost the inspection efficiency through working in the way of continuous scanning. While inspecting the surfaces with multiple connected skeleton curves, the topological complexity brings conflict between achieving efficient inspection and working in continuous manner. Recently, a skeleton curve-guided five-axis sweep scanning method was proposed to tackle this problem but the resulting inspection path has to inspect the entire surface in a round-trip way. The manner of round-trip inspection pulls down the entire inspection efficiency and should be avoided as much as possible. In this paper, we present an improved skeleton curve-guided five-axis sweep scanning method to generate a more efficient five-axis scanning path for the surface with multiple connected skeleton curves. The proposed method starts from the framework of existing skeleton curve-guided five-axis sweep scanning method. Under the unique kinematic requirements of efficient five-axis sweep scanning, an integer linear programming optimization approach is utilized to optimally connect the inspection paths on independent surface patches and form a shorter skeleton curve-based sweep scanning path as compared with the existing skeleton curve-guided five-axis sweep scanning method. The resulting inspection path is composed of the single-pass inspection for most of the surface and the round-trip inspection for a small part of the surface. The comparison experiments are conducted on two surfaces with multiple connected skeleton curves. Experiment results show that the proposed method significantly outperforms the method provided by the leading commercial software Apexblade and the original skeleton curve-guided five-axis sweep scanning method.

A Flow-Based Formulation of the Travelling Salesman Problem with Penalties on Nodes

The travelling salesman problem (TSP) is one of combinatorial optimization problems of huge importance to practical applications. However, the TSP in its “pure” form may lack some essential issues for a decision maker—e.g., time-dependent travelling conditions. Among those shortcomings, there is also a lack of possibility of not visiting some nodes in the network—e.g., thanks to the existence of some more cost-efficient means of transportation. In this article, an extension of the TSP in which some nodes can be skipped at the cost of penalties for skipping those nodes is presented under a new name and in a new mathematical formulation. Such an extension can be applied as a model for transportation cost reduction due to the possibility of outsourcing deliveries to some nodes in a TSP route. An integer linear programming formulation of such a problem based on the Gavish–Graves-flow-based TSP formulation is introduced. This formulation makes it possible to solve the considered problem by using any integer linear programming optimization software. Numerical examples and opportunities for further research are presented.

Optimizing design and performance assessment of a sustainability hydrogen supply chain network: A multi-period model for China

The potential future use of hydrogen as transportation fuel should be assessed from supply chain point. However, the main challenge to motivate hydrogen supply chain (HSC) network is the insufficiency of the present hydrogen infrastructure. This paper presents a mixed integer linear programming (MILP) optimization model for a rational HSC design, given that primary energy source availability, production technologies, transportation modes and storage types. A sustainable principle based on Markov chain is innovatively introduced in the design of HSC network. The strategic decisions for sustainability infrastructure plans can be hierarchically analyzed in time-associated steps (2025–2030–2035). Meanwhile the spatiotemporal-resolved design is further used for an explicit consideration of specific city (Dalian in China), not heavily focused on national/country scale. Furthermore, optimizations are evaluated with total daily costs, carbon tax and CO2 emissions reduction constraints. The results show that the route of WE(S2)→WE(S2)→WE(S3) is the strategic decisions for Dalian hydrogen infrastructure plan from economic-environmental sustainable viewpoint, with the whole HSC costs of 18.44 $ kg−1 H2 in 2025, 10.56 $ kg−1 H2 in 2030 and 5.98 $ kg−1 H2 in 2035. Finally, the proposed design can provide policy-makers with the selection of sustainability pathways for dynamic hydrogen development planning.

Model predictive control of sun-coupled innovative heat pumps: a comparison of economic and environmental optimizations

Background: Heating and cooling in buildings represents a significant amount of the energy demand in the EU, but the market penetration of renewable solutions is still marginal. The SunHorizon project aims at proving the viability and benefits of innovative coupling between heat pumps and various advanced solar panels. Methods: This study focuses on the optimal operation strategies of a technological package composed of hybrid photovoltaic thermal (PVT) panels, a gas driven heat pump and a hot water storage tank. In this work, a model predictive control is developed, based on mixed integer linear programming (MILP) optimization. This model uses innovative elements compared to traditional model predictive control (MPC), with environmental indicators for the electricity grid accounting for imports, co-simulation with TRNSYS using the transmission control protocol (TCP) and modelling of inter-seasonal storage for long and short-term decisions. The usual minimization of costs is compared to two new optimization approaches, which aims to minimize greenhouse gas (GHG) emissions and maximizing renewable use. Results and conclusions: The results of the optimization of costs and GHG emissions show that gains can be found within the variations in time series related to the electricity grid, but the overall operation strategies remain similar. Optimization of renewable share and self-consumption is another path for control strategy, but with less economic and environmental performance.

Early DSE and Automatic Generation of Coarse-grained Merged Accelerators

Post-Moore’s law area-constrained systems rely on accelerators to deliver performance enhancements. Coarse-grained accelerators can offer substantial domain acceleration, but manual, ad hoc identification of code to accelerate is prohibitively expensive. Because cycle-accurate simulators and high-level synthesis (HLS) flows are so time-consuming, the manual creation of high-utilization accelerators that exploit control and data flow patterns at optimal granularities is rarely successful. To address these challenges, we present AccelMerger, the first automated methodology to create coarse-grained, control- and data-flow-rich merged accelerators. AccelMerger uses sequence alignment matching to recognize similar function call-graphs and loops, and neural networks to quickly evaluate their post-HLS characteristics. It accurately identifies which functions to accelerate, and it merges accelerators to respect an area budget and to accommodate system communication characteristics like latency and bandwidth. Merging two accelerators can save as much as 99% of the area of one. The space saved is used by a globally optimal integer linear program to allocate more accelerators for increased performance. We demonstrate AccelMerger’s effectiveness using HLS flows without any manual effort to fine-tune the resulting designs. On FPGA-based systems, AccelMerger yields application performance improvements of up to 16.7× over software implementations, and 1.91× on average with respect to state-of-the-art early-stage design space exploration tools.

Routing Heterogeneous Traffic in Delay-Tolerant Satellite Networks

Delay-tolerant networking (DTN) offers a novel architecture that can be used to enhance store-carry-forward routing in satellite networks. Since these networks can take advantage of scheduled contact plans, distributed algorithms like the Contact Graph Routing (CGR) can be utilized to optimize data delivery performance. However, despite the numerous improvements made to CGR, there is a lack of proposals to prioritize traffic with distinct quality of service (QoS) requirements. This study presents adaptations to CGR to improve QoS-compliant delivery ratio when transmitting traffic with different latency constraints, along with an integer linear programming optimization model that serves as a performance upper bound. The extensive results obtained by simulating different scenarios show that the proposed algorithms can effectively improve the delivery ratio and energy efficiency while meeting latency constraints.

Advances in Survivability-supported Framework for Traffic Engineering in Multi-service Backbone Networks

In this paper, optimisation models and heuristic algorithms that address the off-line survivability-supported Traffic Engineering (TE) problem in multi-service backbone networks are presented. In such networks traffic demands with different Quality of Service (QoS) and survivability requirements (e.g. existence of a node disjoint backup path for each primary path) inhere. The optimisation models for engineering the QoS traffic with different survivability prerequisites and the Best-Effort (BE) traffic are based on novel admission control/routing Integer Linear Programming (ILP) and Linear Programming (LP) optimisation sub-problems, which are solved sequentially. LP relaxations of the ILP sub-problems are also provided. The optimisation models and heuristic algorithms are tested on two different networks and their performance is compared.

Graph-Based Specification and Automated Construction of ILP Problems

In the Model-Driven Software Engineering (MDSE) community, the combination of techniques operating on graph-based models (e.g., Pattern Matching (PM) and Graph Transformation (GT)) and Integer Linear Programming (ILP) is a common occurrence, since ILP solvers offer a powerful approach to solve linear optimization problems and help to enforce global constraints while delivering optimal solutions. However, designing and specifying complex optimization problems from more abstract problem descriptions can be a challenging task. A designer must be an expert in the specific problem domain as well as the ILP optimization domain to translate the given problem into a valid ILP problem. Typically, domain-specific ILP problem generators are hand-crafted by experts, to avoid specifying a new ILP problem by hand for each new instance of a problem domain. Unfortunately, the task of writing ILP problem generators is an exercise, which has to be repeated for each new scenario, tool, and approach. For this purpose, we introduce the GIPS (Graph-Based ILP Problem Specification Tool) framework that simplifies the development of ILP problem generators for graph-based optimization problems and a new Domain-Specific Language (DSL) called GIPSL (Graph-Based ILP Problem Specification Language) that integrates GT and ILP problems on an abstract level. Our approach uses GIPSL specifications as a starting point to derive ILP problem generators for a specific application domain automatically. First experiments show that the derived ILP problem generators can compete with hand-crafted programs developed by ILP experts.

マイクログリッドシステムの運転におけるMILPとPSOを用いたハイブリッド手法の実用性評価

Mixed integer linear programming (MILP) and particle swarm optimization (PSO) are applied to create operational plans for microgrid systems composed of photovoltaic, battery, and co-generation systems. In this study, yearly operation plans are created using either MILP or PSO method with different optimization characteristics to minimize the operational cost, and evaluates the practicality of each method in terms of calculation accuracy and calculation time. Furthermore, a novel hybridized method is developed using the two calculation methods, and its usefulness is evaluated.

A Novel Strategy for VNF Placement in Edge Computing Environments

Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met.

Time Delay Unit Architecture Optimization for Phased Antenna Arrays Using Integer Linear Programming

A systematic method for optimizing the time delay unit architecture (TDU-A) of a phased antenna array (PAA) is introduced. The problem is cast in the standard form of integer linear programming optimization with an objective function that targets minimizing the total number of TDUs within the RF fan-out while maintaining phase error and manufacturability constraints. The presented optimization method can reduce the cost, power, and complexity of a broadband PAA in contrast to prior methods that converge on a manufacturable architecture without considering the total number of TDUs. Three optimization examples are presented with a requirement of a maximum of 5° phase error. These examples clearly show that there are many TDU-As that can satisfy the phase error requirement, yet only one architecture is superior by exhibiting the minimum number of TDUs. The superior architecture is highly likely to be missed in a traditional design approach that places the TDUs in the RF fan-out as close as possible to the antenna elements if the presented optimization approach is omitted.

To Join or Not to Join? The Energy Community Dilemma: An Italian Case Study

Energy Communities (EC) are becoming a major driver to foster the energy transition in Europe and the regulatory framework adopted by each Member State (MS) plays a key role for a prosperous deployment of ECs. This paper is thus divided into two layers. The first layer of this paper addresses the current regulations introduced by MSs regarding ECs, providing a critical comparison of each solution used. The second layer of research concerns the introduction of a Mixed Integer Linear Programming (MILP) optimization algorithm early studied by some of the authors furtherly developed to assess the conditions that favour prosumers’ participation to ECs. Both these models have been tested on a case study located in the city of Magliano Alpi, in the north of Italy. The results demonstrate that the proposed methodology correctly evaluates the key parameters influencing participation of citizens in ECs and indicate that for the Italian EC under study, there is the possibility to further expand the capacity installed without undermining the profitability of investment.

A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters

In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC; Leadville, CO; and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.