A Novel Design of an Automation System for an Energy-Efficient Polygeneration Plant

Polygeneration plants are a leading means of increasing the efficiency and reducing the environmental impact of energy conversion. This study addresses the lack of studies on automation and control of systems that produce electricity, heating, cooling, and potable water. Mixed integer linear programming (MILP) optimization of the annualized total cost is used to select and size the plant components. The final layout consists of solar photovoltaic (PV) and thermal, an absorption chiller and a multi-effect desalination for electricity, heating, cooling and water, respectively. As auxiliary sources, boiler, gas turbine and grid connection are present. Storage units for electricity, heating, and water are also included in the proposed layout. The control and automation system are implemented using ladder logic. Sufficient supply, efficient resource use, and financial gain are considered in the control scheme design. Applying the methodology to a mixed industrial, commercial and residential zone in Southern Luzon, Philippines, the simulation results show successful implementation of all control scenarios. Economic analysis on the hourly cash flows of the system on a typical day show significant daily savings that outweigh the investment cost of the proposed control and automation system with monthly savings of ₱25,112.40 and payback period of 0.4 years.

Reverse logistics network design for a biogas plant: An approach based on MILP optimization and Analytical Hierarchical Process (AHP)

Cyber-security is a rising issue for automotive electronic systems, and it is critical to system safety and dependability. Current in-vehicles architectures, such as those based on the Controller Area Network (CAN), do not provide direct support for secure communications. When retrofitting these architectures with security mechanisms, a major challenge is to ensure that system safety will not be hindered, given the limited computation and communication resources. We apply Message Authentication Codes (MACs) to protect against masquerade and replay attacks on CAN networks, and propose an optimal Mixed Integer Linear Programming (MILP) formulation for solving the mapping problem from a functional model to the CAN-based platform while meeting both the security and the safety requirements. We also develop an efficient heuristic for the mapping problem under security and safety constraints. To the best of our knowledge, this is the first work to address security and safety in an integrated formulation in the design automation of automotive electronic systems. Experimental results of an industrial case study show the effectiveness of our approach.

Cyber-security is a rising issue for automotive electronic systems, and it is critical to system safety and dependability. Current in-vehicles architectures, such as those based on the Controller Area Network (CAN), do not provide direct support for secure communications. When retrofitting these architectures with security mechanisms, a major challenge is to ensure that system safety will not be hindered, given the limited computation and communication resources. We apply Message Authentication Codes (MACs) to protect against masquerade and replay attacks on CAN networks, and propose an optimal Mixed Integer Linear Programming (MILP) formulation for solving the mapping problem from a functional model to the CAN-based platform while meeting both the security and the safety requirements. We also develop an efficient heuristic for the mapping problem under security and safety constraints. To the best of our knowledge, this is the first work to address security and safety in an integrated formulation in the design automation of automotive electronic systems. Experimental results of an industrial case study show the effectiveness of our approach.

This work compares two optimization approaches for combined cooling, heating and power (CCHP or Tri-generation) energy systems scheduling. Both approaches are developed through dedicated software codes and are based on simulation models capable of evaluating of the best operating strategy (both economically and energy-wise) to run a given trigeneration plant while dealing with time-variable loads and tariffs. The simultaneous use of different prime movers operating in parallel is taken into consideration as well as their part load performance, the influence of ambient temperature and the usage of a heat storage system. Cooling may be generated through absorption chillers or electrically driven compression cycles. One of the models is heuristic and adopts an optimization strategy based on a multi-step approach: it simulates several cases according to a pre-defined number of paths, exploring the most reasonable operational modes and comparing them systematically. The other relies on a mathematical approach, based on a Mixed Integer Linear Programming (MILP) optimization model which has been developed in order to deal with more complex systems without the need of predefining a too large variety of operation paths. Results of the two models are compared against a test case based on real plant specifications, discussing their performance by the point of view of simulation capabilities, quality and accuracy of the optimization results (in terms of differences in energy and economic performance) and computational resources.

This study aims to derive simple formulas for calculating the accurate Total Harmonic Distortion (THD) of Multilevel Inverter (MLIs), that considers all low and high order harmonics, for single phase and three phase MLIs., since the THD is a main criterion when judging the performance of a MLI. In addition, a formula for the percentage route mean square value of high order harmonics %V HOrms is derived. These formulas could be applied for all types of MLIs that use different switching techniques for harmonics elimination or reduction, so long as the switching angles of the phase voltage are known and it is specially suitable to be applied with the Mixed Integer Linear Programming (MILP) optimization model, which is constructed for determining the switching angles of the MLI that minimize the absolute values of any undesired harmonics. This study aims also to show that this MILP model can produce solutions that have low values of the accurate THD, by giving the detailed solutions of two cases taken from the references.

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

Smart energy systems can mitigate electric interruption costs provoked by manifold disruptive events via making efforts toward proper pre-disturbance preparation and optimal post-disturbance restoration. In this context, effective contingency management in power distribution networks calls for contemplating disparate parameters from interconnected electric and transportation systems. This chapter, while considering transportation issues in power networks’ field operations, presents a navigation system for pre-positioning resources such as field crews and reconfiguring the network to acquire a more robust configuration in advance of the imminent catastrophe. Also, after the occurrence of the calamity, this navigator optimally allocates the resources to recover the devastating system. So, providing a coordination framework for manual field operation and automation system, this navigator takes a step from traditionally operated systems accommodation toward smart networks. During the contingency management process, there might be modifications in initial data due to the dynamic and time-varying condition of electric and transportation systems. Therefore, the mentioned navigator copes with a real-time problem of data-driven decision making in which, the decisions need to track online changes to the input data. Decision making by the navigation system in this environment is based on a mixed integer linear programming (MILP) optimization which is described in this chapter in detail.

To reduce the energy consumption of the Internet, much research effort has been dedicated to the design of energy-minimized IP over WDM networks. In this study, we design energy-minimized IP over WDM networks based on modular router cards and by jointly applying the lightpath bypass and router-card sleeping strategies. As a key advantage, the design does not require rerouting of IP flows or optical channels in either the IPor optical layer. Rather, to save energy, we only need to sleep or wake up the router cards according to user traffic demands when applying the sleeping strategy, which significantly simplifies the network control and management. To minimize the total energy consumption of the IPover WDM network in a time period (e.g., 1 day), we develop a mixed integer linear programming (MILP) optimization model. Moreover, to alleviate the computational complexity of the MILP model, we divide the minimization problem into two subproblems, namely, 1) establishing a virtual topology and 2) allocating router ports on router cards to the optical channels on different virtual links. For each of the subproblems, we develop a separate MILP optimization model. Further, for large-network design, we also propose two efficient heuristics to optimize energy consumption. Simulation studies indicate that the joint MILP model with the lightpath bypass and router-card sleeping strategies can maximally reduce energy consumption, up to 40% compared to the nonsleeping case. In addition, under the router-card sleeping strategy, the approach of allocating the router ports on different router cards to the optical channels plays an important role in the design of an energy-minimized IP over WDM network. A mixed mode that jointly performs interleaving and sequential router-port allocations can achieve the highest energy savings.

Network-on-chip (NoC) has been proposed as a solution for the communication challenges of system-on-chip (SoC) design in the nanoscale regime. SoC design offers the opportunity for incorporating custom NoC architectures that are more suitable for a particular application, and do not necessarily conform to regular topologies. This paper presents novel linear programming based techniques for synthesis of custom NoC architectures. In the nanoscale regime, low power consumption would continue to be an important design goal. We first discuss an optimal mixed integer linear programming (MILP) formulation that synthesizes a low power NoC architecture subject to the performance constraints. The MILP formulation is limited by large run times. We next present heuristic techniques that exploit clustering, and 0-1 constraint relaxation to reduce the run times of the formulation. The techniques minimize power as the primary goal, and minimize the number of routers (area) as a secondary goal. We present an analysis of the quality of the results and the solution times of the proposed techniques by extensive experimentation with the realistic benchmarks. The clustering based heuristic generates results whose power consumption is within 11% of the MILP solutions and its average run time is 171.1 seconds. The average run time of the relaxation and rounding based techniques is less than 2 seconds, and the power consumption of their solutions is within 58% of the MILP result.

This paper presents a deterministic capacity expansion optimisation model designed for large regional or national water supply systems. The annual model selects, sizes and schedules new options to meet predicted demands at minimum cost over a multi-year time horizon. Options include: supply-side schemes, demand management (water conservation) measures and bulk transfers. The problem is formulated as a mixed integer linear programming (MILP) optimisation model. Capital, operating, carbon, social and environmental costs of proposed discrete schemes are considered. User-defined annual water saving profiles for demand management schemes are allowed. Multiple water demand scenarios are considered simultaneously to ensure the supply–demand balance is preserved across high demand conditions and that variable costs are accurately assessed. A wide range of supplementary constraints are formulated to consider the interdependencies between schemes (pre-requisite, mutual exclusivity, etc.). A two-step optimisation scheme is introduced to prevent the infeasibilities that inevitably appear in real applications. The model was developed for and used by the ‘Water Resources in the South East’ stakeholder group to select which of the 316 available supply schemes (including imports) and 511 demand management options (considering 272 interdependencies) are to be activated to serve the inhabitants of South East of England. Selected schemes are scheduled and sized over a 25 year planning horizon. The model shows demand management options can play a significant role in the region’s water supply and should be considered alongside new supplies and regional transfers. Considering demand management schemes reduced overall total discounted economic costs by 10 % and removed two large reservoirs from the least-cost plan. This case-study optimisation model was built using a generalised data management software platform and solved using a mixed integer linear programme.

Effect of transmission impairments in CO-OFDM based Elastic Optical Network design

Intelligent buildings are responsible for ensuring indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events. An emerging environmental application involves the monitoring of intelligent buildings against harmful events by incorporating various sensing technologies and using sophisticated algorithms to detect and isolate such events. In this context, both centralized and distributed approaches have been proposed, with the latter having significant benefits in terms of complexity, scalability, reliability, and performance. This paper considers the automatic partitioning of the building into subsystems, which enables the distributed simulation, modeling, analysis, and management of the intelligent building while ensuring the effective detection and isolation of contaminants in the building interior. Specifically, we develop both a high-quality heuristic algorithm and an optimal mixed integer linear programming (MILP) formulation for the building partitioning problem. The MILP formulation is based on graph partitioning techniques, while the heuristic is based on matrix clustering techniques. Both approaches partition the building into subsystems while ensuring 1) maximum decoupling between the various subsystems, 2) strong connectivity between the zones of each subsystem, and 3) control of the size of the subsystems with respect to the number of allocated zones. A combination of the two approaches is also proposed for reconfiguring an initial partitioning composition in real time in order to accommodate partitioning needs that arise from dynamic system changes.

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.

Self-aligned multiple patterning, due to its low overlay error, has emerged as the leading option for 1-D gridded back-end-of-line (BEOL) in sub-14-nm nodes. To form actual routing patterns from a uniform “sea of wires,” cut masks are needed for line-end cutting or realization of space between routing segments. The line-end cutting results in nonfunctional (i.e., dummy fill) patterns that change wire capacitance, and hence design timing and power. Therefore, to remove such dummy fill patterns, extra 2-D block masks are used. However, 2-D block masks cannot remove arbitrary dummy fill patterns, due to design rule constraints on the block mask shapes. In this paper, we address the timing-aware optimization of 2-D block mask layouts under various sets of mask rules that are derived from mask patterning technology options (e.g., 193i and 193d) for foundry 7-/5-nm (N7/N5) BEOL. Our central contribution is a mixed integer linear programming (MILP) optimization that minimizes timing impact due to dummy metal segments while satisfying block mask rules and metal density constraints. We also propose a distributed optimization flow to improve the scalability. With our optimizer, we recover up to 84% of the worst negative slack impact from dummy segments, with up to 64% dummy removal rate. We further extend our MILP to a co-optimization of cut and block masks. This paper gives new insights into fundamental limits of benefit from emerging cut and block mask technology options.