Minimum System Cost Research Articles

Improving efficiency of the queueing system with two types of customers by service decomposition

<abstract><p>The economic improvements of a queueing system with two types of customers achieved by service decomposition are examined. The service process for a Type 2 customer can be split into two phases: a basic service and an additional service. The basic service rate is equal to that of the Type 1 customer. Additional services can be viewed as orders stored in inventory and processed when the server is idle. Once a new customer arrives during idle time, the server will provide a basic service to the customer upon his/her arrival. That is, customers have preemptive priority for orders stored in inventory. We obtain a two-dimensional unbounded Markov process and apply the multivariate generating function to derive the stationary probability of the proposed model as well as some performance measures. The condition under which performing service decomposition can improve economic efficiency is also obtained. Both the optimal inventory capacity and the minimum system cost are obtained numerically. Numerical experiments demonstrate the impact of optimal inventory setting on economic improvement efficiency. Finally, simulation experiments prove the correctness of our theoretical analysis.</p></abstract>

Task Co-Offloading for D2D-Assisted Mobile Edge Computing in Industrial Internet of Things

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

A Three-Stage Stochastic Framework for Smart Electric Vehicle Charging

As one of the most significant part of carbon neutralisation, the rapid growth of electric vehicle (EV) market in past few years has greatly expedited the transport electrification, which, however, has brought in new challenges to power system including isolated distribution network for commercial and industrial set up. Stochastic and complex EV behaviours would violate network permissible operation region and increase costs for system operators. To address these problems, a chance-constrained smart EV charging strategy in a DC microgrid (DCMG) supporting large office complex is proposed to minimize system cost from distribution network and fleet battery degradation cost from EVs providing ancillary service to the DCMG. When dealing with uncertainties from EVs, a Markov Chain Monte Carlo (MCMC) model is built to couple different parameters in load profiles and characterize the time series of likelihood of charging and discharging. A state-of-charge (SOC) space random walk method is then proposed to solve the resultant massive recursive probabilistic charging requirements. Based on that, a three-stage optimization framework is established to illustrate the work flow in system level. Numerical results verifying the effectiveness of the proposed method are also presented.

Prosumer Segmentation Strategies for Local Electricity Market Partaking by Monetary Reward

In order to pave the way for a modernized electricity grid with distributed energy sources and storage, a direct energy transaction among peers in the local electricity market (LEM) is emerging. The design of the local energy market has frequently concentrated on its structures and technological problems, but not much on how to increase the willingness of customers to participate by using a customer-centric approach. In this article, we provide consumer engagement strategies in LEM with the focus on maximizing financial return while exchanging energy with peers. The empirical investigation is subjected to two stages of optimization, the first of which has a goal of minimizing system costs and the second of which has a goal of maximizing consumer rewards. We present four methods for rewarding customers financially for taking part in LEM. A segmentation of customers is proposed to achieve profit maximization goal. The system cost is calculated for two cases and cost saving is used to distribute among the market participants. The proposed LEM model results in 12.87% cost saving which is used to incentivize the active participants of market. Thus policy makers can be recommended to integrate such market mechanism to ensure flexible and efficient market mechanism.

Modelling medium- and long-term purchasing plans for environment-orientated container trucks: a case study of Yangtze River port

Abstract The transportation sector is the most significant contributor to anthropogenic greenhouse gas (GHG) emissions. Particularly, maritime transportation, which is predominantly powered by fossil-fuel engines, accounts for more than 90% of world freight movement and emits 3% of global carbon dioxide (CO2) emissions. China is the world's largest emitter of CO2 and plays a key role in mitigating global climate change. In order to tackle this pressing concern, this study analyses the port's throughput, the current number of trucks and their emissions during the container truck purchasing process. Previous studies about container truck purchasing plans mostly focused on the trucks' price and port needs. The objective of this study is to minimize the total cost of a port's inland transportation using optimization technique such as the interval uncertainty planning model to convert container truck emissions into social costs. The study considers the port of Yangtze as a case study. The study has designed two scenarios. (i) The base scenario (business-as-usual, BAU) is used to quantify the relationship between pollutant emissions and system cost. In the base scenario, no environmental control facilities are used during the planning period, and there is no need to purchase new energy container trucks. (ii) The expected scenario (Scenario A) is for three planning periods. In Scenario A, the emissions levels are required to remain at the same level as the first planning period during the whole planning period. By solving the above model, the number of all truck types, system cost, container throughput and truck emissions in the port area were analysed. The results showed that if no emission reduction control measures are implemented in the next 9 years, the growth rate of pollutants in the port area could reach 20%. In addition, the findings showed clearly that truck emissions are reduced by purchasing new energy trucks and restricting the number of fossil-fuel (diesel) trucks. This study could also help to minimize system costs associated with port planning and management.

Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China

This paper proposes an optimal sizing design and cost-benefit evaluation framework for stand-alone renewable microgrid system to serve rural community load usage in Northeast China. The microgrid system combines Photovoltaic arrays (PV), Wind turbines (WT), Tidal turbines (Tid), Battery (Bat) storage and hydrogen storage, respectively. The optimal component sizing is determined based on the actual local hourly meteorological data and load demand during a year using Chameleon swarm algorithm (CSA), with minimum Total net present cost (TNPC) of the microgrid system as the optimization objective and the reliability constraint is considered as Loss of power supply probability (LPSP). Meanwhile, a cost-benefit index is further introduced for conveniently evaluating the overall effectiveness of the microgrid systems. The results of the simulation experiments under the benchmark scenario reveal that the PV/WT/Tid/Bat system is the most viable approach for local load supply among different versions of the microgrid. Additionally, a thorough sensitivity investigation of the optimal sizing of two exemplary microgrid systems based on different energy storage is carried out under different techno-economic scenarios. This study can provide a more efficient and clean electrification scheme and insightful reference for investment in the power-starved Northeast China.

A bi-level optimal scheduling model for new-type power systems integrating large-scale renewable energy

Abstract The construction of a new type of power system is a key way to achieve the goal of ‘carbon peaking and carbon neutrality’. In the process of developing renewable energy, large-scale wind and photovoltaic power systems replace conventional units, which will have an impact on the stability of the power grid. To eliminate these concerns, this paper proposes a bi-level optimal scheduling model for new-type power systems. The upper level aims to minimize fluctuation of net load, with the consideration of demand response, to smooth the load curve. The lower level takes the minimum operating cost of the new-type power system as the objective function and the most economical scheduling strategy is established. To deal with the uncertainty of renewable energy integrated within the system, the lower model is transformed into a two-stage robust optimization model and solved by applying a column-and-constraint generation method. Finally, a regional power system is selected to conduct a case study comprising 760 MW thermal power, 50 MW·h energy storage, 200 MW wind turbine and 150 MW photovoltaic power. The results show that the constructed two-stage robust optimization model is conducive to establishing the most economical scheduling scheme of the system with an uncertain budget and the minimum operation cost is $45,9031. In addition, demand response and energy storage can reduce peak-to-valley differences in the power system.

Optimal configuration of wind storage capacity based on VMD and improved GWO

In view of the fluctuation of the output power of wind power generation, a hybrid energy storage capacity optimization configuration strategy combining variational mode decomposition (VMD) and improved Grey Wolf Optimizer (GWO) is proposed, taking the wind storage joint system as the research object. The sliding average method is used to obtain the target power of the wind power grid connection and use the VMD decomposition processing to allocate the energy storage target power to the different energy storage devices in the hybrid energy storage system. The capacity optimization model of hybrid energy storage system with the minimum annual configuration cost of energy storage system as the target function is established, and the improved Grey Wolf Optimizer is used to solve the model, and the simulation and analysis are conducted on Matlab / Simulink, so as to verify the effectiveness and economy of the proposed capacity optimization configuration method.

Performance comparison of black liquor gasification and oxidation in supercritical water from thermodynamic, environmental, and techno-economic perspectives

The black liquor poses a great threat to the environment and it is also a kind of available biomass resources. By the supercritical water gasification (SCWG) and supercritical water oxidation (SCWO) technology, the black liquid with high water content can be converted into hydrogen-rich gas or heat energy. In this study, a pulp mill integrated with black liquor SCWG and SCWO subsystems, which have a processing capacity of 11.2 t/h of black liquor with a concentration of 15.2 wt%, is designed. The thermodynamic analysis, techno-economic analysis (TEA), and life cycle assessment on the system are conducted. The gasification characteristics and thermodynamic performance of the SCWG and SCWO systems under different working conditions are studied. The thermodynamic analysis shows that the highest energy efficiencies of SCWG and SCWO systems are 41.53 % and 19.89 %, respectively. The lowest processing cost of black liquor is 398.68 RMB/t, which is obtained in the SCWO system at 600 °C. At 800 °C, the minimum hydrogen production cost of SCWG system is 23.28 RMB/kg H2. The LCA results show that the environmental benefits of SCW system at high temperatures are significantly better than those of integrated black liquor combustion systems. Finally, a multi-attribute decision making results of the SCWG and SCWO systems show that the optimal reaction temperature is 800 °C.

Design and analysis of a photovoltaic‐powered charging station for plug‐in hybrid electric vehicles in college campus

Photovoltaic-powered electric transportation systems are gaining global momentum owing to their superior enactment and zero carbon emissions. With a growing number of electric vehicles (EVs) on the road, implementation of efficient and well-organised charging stations is extremely indispensable. The authors investigate the feasibility of creating a charging station for plug-in-hybrid EVs at an educational institution, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India (10°48.2′N, 79°50.1′E). The statistics related to the solar radiations of the Nagapattinam region are employed to find out the energy availability for the EV charging station (EVCS) and the requirement for grid connection. The authors use a hybrid optimisation model for an electric renewable (HOMER) simulator to determine the optimum specification of the proposed EVCS. Also, the authors carry out a techno-economic study against the specified load requirement to evaluate the potential of the charging station. The solar irradiance data obtained from NASA ground climatology and the solar power dataset of the designated site are used to realise the precise fallouts. The optimisation results provide minimum annual system cost and reliable power to the EVs. Based on these results, an EVCS is erected in the college campus to charge plug-in hybrid electric vehicles. The established EVCS contains 3 kW EV charger operating as a microgrid and includes nine solar panels with 335 W each, 48 V, 150 Ah specification. The authors use solar photovoltaic (PV) panels using Copper Indium Selenide-Zinc sulfide (CISZS) quantum dots for maximising energy yield from the EVCS. The authors consider that eight different charging profiles for different months are employed for estimating the solar irradiance probability density in the designated site. From this study, the authors can conclude that Gaussian mixer model (G4) profile is optimum for the Nagapattinam region to install a potential and reliable EVCS.

Nash Mechanisms for Market Design Based on Distribution Locational Marginal Prices

Being important information of both network conditions and energy trading, distribution locational marginal prices (DLMPs) are greatly useful for distribution network (DN) management. The goal of this paper is to develop a market mechanism that motivates price-making agents to trade active and reactive power at DLMPs. According to the optimal power flow problem based on second-order cone (SOC) relaxation, we first design the market rules that allow agents to strategically submit proposals of price and power generation/consumption. The market rules induce a noncooperative game between the DN operator and distributed generators. We prove that DLMP pricing can be implemented at any generalized Nash equilibrium (GNE) of the game. We also show that economic properties of strong budget balance, individual rationality, and system cost minimization can be achieved at any GNE. A decentralized algorithm is developed to reach a GNE of the game. Further, based on the convex-concave procedure, an extension of the market mechanism is proposed to cope with the situations that SOC relaxation is inexact. In simulation, the proposed market mechanism is compared with other game-theoretic models, and the economic properties are verified.

Interval-parameter bi-level programming for energy system management under uncertainty: Towards a deep-decarbonized and sustainable future in China

To reach the goal of carbon-neutral society, synergistic approaches based on mathematical models that are capable of planning energy system associated with complex uncertainty and hierarchical structure are desired. In this study, an interval-parameter bi-level programming (IBP) method that is effective for solving hierarchical decision-making problems with conflict objectives and uncertainty is developed. Then, an IBP based China's energy system model (abbreviated as IBP_CES) is formulated for mainland China during 2021–2050, where the higher-level objective of minimizing carbon dioxide (CO2) emission is given priority, following by the lower-level objective of minimizing system cost. Results from IBP_CES show that, although China's energy supply would still rely heavily on fossil fuels, the share of coal would decrease to 33.9% by 2050. Results also disclose that the national CO2 emission would reach the peak value during 2026-2030 and then decline rapidly due to development of renewable energy and promotion of carbon-treatment technology which is beneficial for government to achieve carbon neutrality goals. In addition, compared to the traditional single-level model, CO2 emission from IBP_CES can decrease by 5.9%, demonstrating that IBP_CES is more superior in energy system decarbonization. The findings can help decision makers to well understand the interactions and tradeoffs between climate mitigation and economic development, and provide effective information on clean, sustainable and deep-decarbonized development strategies.

Task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach

This article examines a multi-user mobile edge computing (MEC) system for the Internet of Vehicle (IoV), where one edge point (EP) nearby the vehicles can help assist in processing the compute-intensive tasks. For the MEC networks, the majority of existing works concentrate on the minimization of system cost of task offloading under the perfect channel estimation, which however fails to consider the practical limitation of imperfect channel estimation (CSI) because of vehicles’ high-mobility. Therefore, the goal of our study is to reduce the delay as well as energy consumption (EC) of computation and communication with imperfect CSI, which are the two significant performance metrics of MEC network. With this aim, we first express the system cost as a form of the linear combination of the delay and EC, and then formulate the optimization problem for the system cost. Moreover, a novel deep approach is proposed, which is integrated by deep reinforcement learning (DRL) with the Lagrange multiplier to jointly minimize the system cost. In particular, the DRL algorithm is employed to obtain the capable offloading strategy, while the Lagrange multiplier is used to obtain the bandwidth allocation. The simulated results are finally presented to show that the devised approach outperforms the traditional ones.

Competing water uses between agriculture and energy: Quantifying future climate change impacts for the Portuguese power sector

Climate change may increase water needs for irrigation in southern Europe competing with other water uses, such as hydropower, which may likely be impacted by lower precipitation. Climate change will also potentially affect the variability and availability of other renewable energy resources (solar and wind) and electricity consumption patterns. This work quantifies the effect of competition for water use between irrigation and hydropower in the future 2050 Portuguese carbon-neutral power sector and under Representative Concentration Pathway 8.5 climate change projections. It uses the power system eTIMES_PT model to assess the combined effects of climate change on the cost-optimal configuration of the power sectorconsidering changes in irrigation, hydropower, wind and solar PV availability. eTIMES_PT is a linear optimisation model that satisfies electricity demand at minimal total power system cost. Results show that, by 2050, climate change can lead to an increase in annual irrigation water needs up to 12% in Tagus and 19% in Douro watersheds (from 2005 values), with substantially higher values for spring (up to 84%). Combining these increased water needs with the expected reduction in river runoff can lead to a decline in summer and spring hydropower capacity factors from half to three times below current values. By 2050, concurrent water uses under climate change can reduce hydropower generation by 26–56% less than historically observed, mainly in summer and spring. Higher solar PV, complemented with batteries’ electricity storage, can offset the lower hydropower availability, but this will lead to higher electricity prices. Adequate transboundary water management agreements and reducing water losses in irrigation systems will play a key role in mitigating climate impacts in both agriculture and power sector.

Charging Infrastructure and Schedule Planning for a Public Transit Network with a Mixed Fleet of Electric and Diesel Buses

Promoting battery electric buses (BEBs) can reduce fuel consumption and air pollution from the transit system. A complete transition from the current diesel fleet to BEBs is costly and time-consuming. Thus, the intermediate solution is a combination of diesel, hybrid, and BEBs. Therefore, a planning framework is required that simultaneously tackles three contiguous aspects of transit electrification and their interconnections, namely charging infrastructure, fleet configuration, and scheduling. Accordingly, this study considers a mixed fleet of diesel and BEBs. It aims to concurrently find (i) the optimal location and capacity of charging infrastructure, considering micro-grid specifications, the impact of distributed energy resources, and time-of-use electricity rates and (ii) optimum operation and refueling strategies. Another objective of this study is to capture the impacts of adverse weather conditions on transit electrification. A mixed-integer problem is proposed and solved using a metaheuristic algorithm based on simulated annealing to minimize system costs, including infrastructure, fleet, and operation costs. A subnetwork of transit in Worcester, Massachusetts, is selected as a case study, including three routes, five candidate charging locations, and three bus types. Findings suggest that BEBs can operate and serve the passenger demand with sufficient charging infrastructure. Sensitivity analyses show that even though high-power chargers are more expensive per piece, they reduce the overall cost as fewer chargers are required. The cost rises for chargers with power of 350 kW or more. It is worth noting that the benefits of BEBs are more significant in smaller buses and are heavily affected by adverse weather conditions.

A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that this approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

Performance study of transcritical CO2 heat pump integrated with ejector and latent thermal energy storage for space heating

This study proposes a new transcritical CO2 heat pump integrated with ejector and latent heat energy storage system, and compares the performance of the system with and without energy storage equipment based on TRNSYS and experimental results. The results show that the new system operates more stably and is more conducive to space heating. Compared with the transcritical CO2 heat pump system with only ejector, the new system is more affected by the water flow rate and its performance can be improved by 18.53% when the water flow rate is 0.103 kg·s−1. The new system has more energy saving potential in low temperature environment, for which the utilization rate of primary energy and the primary energy ratio could reach 15.33 kgce·m−2 and 1.24, respectively. After thermo-economic optimization, it can be found that the minimum life-cycle cost of the new system is about 51.91 thousand CNY, which is reduced by 11.46% compared with the only ejector system. Therefore, the new system is superior to the previously transcritical CO2 heat pump systems both in terms of thermodynamic performance and economic performance. The research results are helpful to further promote the research and application of air source heat pump for space heating in low temperature area.

(Digital Presentation) Hydrogen Peroxide Electrolyzer and Reversible Hydrogen Peroxide Cycle Cell for Renewable Energy Storage

The rapid increase of intermittent renewable energy such as wind and solar powers requires large-scale, efficient, economic, and zero-emission systems for energy storage.1,2 Energy storage based on the H2-water cycle realized by water electrolyzers and fuel cells has been proposed for large-scale energy storage and grid-balancing.1,3,4 However, the energy efficiency of H2-water cycle is restricted by the sluggish kinetics of the electrochemical oxygen evolution and oxygen reduction reactions in water electrolyzers and fuel cells, respectively.5-7 In this presentation, we will discuss the concept of distributed generation and energy storage systems based on the highly efficient electrochemical cycle of hydrogen peroxide (H2O2).8 In this concept, H2O2 can be electrolyzed to H2 and O2 to store the energy in the short term and then be regenerated via the two-electron oxygen reduction reaction in a fuel cell-type device to generate power. We will also discuss the techno-economical analysis of large-scale, long-term hydrogen and energy storage using H2O2 as the storage media.As a proof of concept, we will summarize our work on proton exchange membrane hydrogen peroxide electrolyzer (PEM-HPEL), which employs platinum group metal-free (PGM-free) catalysts for hydrogen peroxide oxidation reaction at the anode.8 The prototypes of PEM HPEL showed low onset cell voltage of ca. 0.7 to 0.8 V with 0 to 0.1 V overpotentials and a high voltage efficiency of over 90%. We will also present our work on unitized regenerative hydrogen peroxide cycle cells (UR-HPCC) based on PGM-free bifunctional oxygen electrode catalyst.9 This UR-HPCC operates in either HPEL mode or fuel cell mode for energy storage and power generation, with an ultralow overpotential and an extremely high round-trip voltage efficiency of over 90%. The minimal system cost, low power consumption, and high energy efficiency render the above H2O2 electrochemical cycle systems highly attractive for short- and long-term energy/hydrogen storage.

Optimization of the possible pathways for gradual energy system decarbonization

The European Union and other signatories to the Paris Climate Accord have agreed to limit global warming to 2° Celsius above pre-industrial levels. Previously published studies have examined the optimal energy system structure required to satisfy carbon neutrality end goals. This research focuses on addressing the intermediate steps towards decarbonization. The steps are quantified as the percentage share of renewable energy sources. The objective of the optimization is to reach a predetermined level of renewable energy and emissions, minimize curtailment of renewable energy sources, minimize system cost, and limit the use of natural resources such as biomass in the energy sector. Considered technologies in the optimization process are energy-generating capacities, demand response technologies, and energy storage. The results of such a method reflect the use of considered technologies and are displayed as a function of renewable energy share and carbon dioxide emissions level, which also represent the decarbonization timeline from 2020 to 2050. The method is carried out with the use of energy planning software EnergyPLAN and highly modified Python-based optimization software EPLANopt. The results presented in the research display the necessity for continuous implementation of variable generating capacities as well as demand response technologies, mainly vehicle to grid.

Agent-based simulation and simulation optimization approaches to energy planning under different scenarios: A hospital application case

In complex multi-energy systems, one of the main goals is that combining optimal circumstances to acquire minimum system cost as decreasing energy consumption without an outage of energy supply. This study mainly has focused on flexible solutions that include the controlling of a multi-energy system and its components such as electricity, heating, natural gas, and converters to minimize the overall system cost and reduce carbon emissions by observing of demand whether is satisfied. The technique of agent-based simulation and simulation optimization approaches has been adopted as solution methods for tackling these difficulties. As a case study to implement the flexible solutions, the multi-energy system of a large hospital complex placed in Turkey was selected. These flexible solutions based on the integration of cogeneration and photovoltaic systems to existing energy infrastructure have been simulated under multi-tariff and singe-tariff electricity price strategies for different years. The used data in the model has been gathered from the hospital electricity and natural gas bills, the energy projects realized for the hospital in the past, and other various sources including PVGIS database, Turkish State Meteorological Service, commercial cogeneration/PV panel manufacturers, and publicly available reports. The results indicate that the effective presence of cogeneration and photovoltaic is notable in the decreasing total system cost taking into consideration also lower carbon emissions. Once current system was simulated, it was compared with other proposed possible configurations with different two scenarios. Based on the simulation models that were optimized with different metaheuristics such as genetic algoritm, the economic results show that the both of two scenarios can be profitable (simple pay back periods around 5.28 and 6.6 years respectively) even without any public funding. Furthermore, it was determined that cogeneration-based scenario-2 provided a 12.35 % reduction in carbon emissions compared to the base scenario, while scenario-3 provided a 16.94 % reduction compared to the base scenario, and both scenarios are proven to be quite environmental friendly.