System Operation Cost Research Articles

Bibliometric analysis and systematic review of electrochemical methods for environmental remediation

Electrochemical methods are increasingly favored for remediating polluted environments due to their environmental compatibility and reagent-saving features. However, a comprehensive understanding of recent progress, mechanisms, and trends in these methods is currently lacking. Web of Science (WoS) databases were utilized for searching the primary data to understand the knowledge structure and research trends of publications on electrochemical methods and to unveil certain hotspots and future trends of electrochemical methods research. The original data were sampled from 9080 publications in those databases with the search deadline of June 1st, 2022. CiteSpace and VOSviewer software facilitated data visualization and analysis of document quantities, source journals, institutions, authors, and keywords. We discussed principles, influencing factors, and progress related to seven major electrochemical methods. Notably, publications on this subject have experienced significant growth since 2007. The most frequently-investigated areas in electrochemical methods included novel materials development, heavy metal remediation, organic pollutant degradation, and removal mechanism identification. “Advanced oxidation process” and “Nanocomposite” are currently trending topics. The major remediation mechanisms are adsorption, oxidation, and reduction. The efficiency of electrochemical systems is influenced by material properties, system configuration, electron transfer efficiency, and power density. Electro-Fenton exhibits significant advantages in achieving synergistic effects of anodic oxidation and electro-adsorption among the seven techniques. Future research should prioritize the improvement of electron transfer efficiency, the optimization of electrode materials, the exploration of emerging technology coupling, and the reduction in system operation and maintenance costs.

Can synergies in agriculture through an integration of solar energy reduce the cost of agrivoltaics? An economic analysis in apple farming

Agrivoltaics is a relatively new concept of agriculture and photovoltaic power generation on the same area. While an increased land use efficiency and potential synergies between agriculture and power generation speak in favor of agrivoltaics, higher investment cost compared to ground-mounted photovoltaics represents a challenge for a broader market launch in most countries.This work analyses the economic performance of agrivoltaics in apple farming focusing on potential synergies and adverse effects regarding investment requirements and operational cost of the farming system. The analysis is based on literature, expert interviews, and data of three pilot projects in Germany. The results show that average investment cost from the farming system could be reduced by 26% mainly due to partially replacing the hail protection structure. Annual operating costs of the farming system reduce by up to 9% through lower cost for land and maintenance works. However, annual revenues also decrease by about 9% due to an expected reduction in high quality apple yield. Overall, the cost of apple production decreases by about 5%. Regarding the total cost of agrivoltaics, though, the potential contribution from cost savings in the farming sector to reduce the cost of electricity only amounts to <1%. The expected Land Equivalent Ratio of the analyzed agrivoltaic system amounts to 1.54.The results indicate that agrivoltaics in orcharding is only economically feasible if the regulatory framework provides sufficiently high feed-in tariffs or comparable support payments. The work also shows that the theoretical potential of agrivoltaics in apple farming in Germany amounts to 23.8 GWp which could contribute to 13% of the PV development required to meet Germany's climate goals by 2030 [1].

An improved binary African vultures optimization approach to solve the UC problem for power systems

Unit commitment (UC) is one of the most crucial problems in electrical power systems. It concerns finding the optimal schedule for generating units such that the production cost is minimized over a given period. This paper aims to solve the unit commitment problem by proposing a binary version of a recent metaheuristic algorithm, the African Vultures Optimization Algorithm (AVOA), which is a nature-inspired metaheuristic algorithm that mimics the hunting mechanism and behavior of African vultures. This algorithm has the advantage of creating various phase shift approaches to avoid premature convergence and local optimum trapping. AVOA uses two mechanisms for the exploration phase and four for the exploitation phase that ensure the algorithm's ability to diversify and intensify. The AVOA is a continuous algorithm that cannot tackle the mixed-integer nature of the UC. In this paper, a sigmoid transfer function is used to convert the algorithm into a binary algorithm to decide the on/off status of the generating units. To further enhance the performance of the proposed algorithm, new values of the controlling parameters are proposed. Finally, the performance of the proposed algorithm is tested on IEEE 30-, 14-, and 57-bus systems. The proposed Binary AVOA (BAVOA) has superiority over the other algorithms given in the case study. In addition, results revealed that the BAVOA gives better results in the discrete search space (DSS) compared to the continuous search space (CSS). For instance, the operation cost of an IEEE 30-bus system in the DSS is 12,768 dollars which is 7% less than the CSS.

A carbon trading approach for heat-power-hydrogen integrated energy systems based on a Vickrey auction strategy

Exploring the way of the integrated heat-power‑hydrogen energy system (CHHP) participates in carbon trading is important for reducing carbon emissions and achieving carbon peaking and carbon neutrality goals. To fully consider the potential for renewable energy to participate in the carbon trading market through Chinese certified emission reductions (CCERs), this paper proposes a carbon trading method for CHHP based on a Vickrey auction strategy. Firstly, the model of CHHP is built by considering the waste heat of the hydrogen power generation system. The calculation of CCERs of hydrogen and other renewable energy sources is then studied based on calorific value and combined marginal emission factors. Meanwhile, given the zero carbon of renewable energy sources and the large difference in price between Chinese emission allowances (CEAs) and CCERs. For traditional energy sources, a baseline approach is used to allocate CEAs to carbon sources. A price-fixed trading model for CEAs is established considering actual carbon emissions; For the CCERs from hydrogen and other renewable energy, a trading mechanism based on a Vickrey auction has been introduced. Finally, the proposed method is analyzed in three scenarios. The approach proposed in this paper results in lower system operation costs and more favorable planning for low-carbon emission output equipment. The results show that the proposed trading method has advantages in the economy and carbon emissions.

Frequency-based demand side response considering the discontinuity of the ToU tariff

Demand side response (DSR) according to the time-of-use (ToU) tariff can bring a number of benefits to consumers and networks. However, for microgrids with high penetrations of renewable energy and flexible load, the sudden and drastic variations of loads triggered by the discontinuity of the ToU tariff could be fatal to the frequency stability of this low inertia system. This paper proposes a new frequency-based DSR (FB-DSR) strategy through a two-stage model with the frequency dynamics expressed analytically as the exchange power. The first stage optimizes the day-ahead DSR strategy based on the ToU tariff and equipment degradation cost. At the second stage, frequency response is constrained through the whole process of inertia support, frequency nadir and quasi-steady-state by integrating time-domain optimization and frequency-domain control. Numerical results demonstrate that the proposed strategy can effectively reduce the volatility of flexible load when responding to the ToU tariff, and thus significantly lowering the cost of maintaining the system frequency stability. At the same time, the negative impact of this strategy on the economic benefits of DSR is negligible. The overall operating cost of the test system is reduced by 7.34% even considering the increased degradation cost.

An intelligent network approach to renewable energy management decisions: A comparative study of integrated energy system

This paper presents an analysis of how intelligent network approaches can be used to inform renewable energy scheduling decisions. Integrated energy system (IES) optimum planning problems are characterized by high dimensional nonlinearity. To this end, an electricity and heating combined demand response (DR) scheme is proposed that takes dispatchers into account when calculating dispatch capabilities. This study constructs a load group aggregation scheme based on incentive DR and constructs the electrical load response scheme to minimize deviations between the dispatch signal and the characteristic model. Continuous intelligent deep searching can be realized by jumping outside the local optimum and improving the algorithm's capability to do so. Chicken swarm optimization is deployed as the optimizer and it is shown that it is more effective in comparison to the conventional intelligent optimizer. The paper also identifies key elements of a successful network approach based on digital twin and discusses how the proposed approach can be used to inform renewable energy decisions in other contexts. As a result of incorporating the dispatcher's preferred signal into the integrated DR, new energy is accommodated and the system's operational costs are reduced, as well as the resource-load relationship is improved.

An Application Designed for Guiding the Coordinated Charging of Electric Vehicles

Guiding the coordinated charging of electric vehicles can alleviate the load fluctuation of power systems within a local area caused by uncoordinated charging of electric vehicles and greatly reduce the cost of power system operation. This will become an inevitable development trend of future energy system transformation. In this paper, a new mobile application is built to realize the dynamic adjustment of electric vehicle charging prices according to the change in weather conditions to guide the coordinated charging of electric vehicles. After systematically introducing the structure and data flow process of the application, we simulate the fluctuation of charging prices under various weather conditions using the electricity load data of North China and verify the good performance of the application. We believe that this application can help power systems to achieve low-carbon transformation by adopting a new dynamic time-of-use pricing charging model.

A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism

This paper proposes a hybrid energy storage system model adapted to industrial enterprises. The operation of the hybrid energy storage system is optimized during the electricity supply in several scenarios. A bipolar second-order RC battery model, which can accurately respond to the end voltage, (State of charge) SOC, ageing mechanism and other characteristics of the battery, is established. The batteries and the supercapacitor consist of a hybrid energy storage system. The system operation cost and the battery cycle life are investigated. This paper realizes energy scheduling through load prediction technology. The proposed energy scheduling strategy plans the operation of the hybrid energy storage system and reduces the frequency of the battery's charging and discharging. The results show that the proposed prediction model keeps the hybrid energy storage model's overall electric load prediction accuracy up to 97.12%–98.89%. Combining the load prediction technique with the optimal scheduling strategy, the decay of lithium battery capacity of 120kwh to 96.16kwh is better than the decay of battery capacity of 120kwh to 87.32kwh under no scheduling strategy set. The total economic cost per quarter is reduced by $20,000-$35,000.

Five Phases Algorithm: A Novel Meta-heuristic Algorithm and Its Application on Economic Load Dispatch Problem

&lt;p&gt;A new meta-heuristic algorithm named the five phases algorithm (FPA) is presented in this paper. The proposed method is inspired by the five phases theory in traditional Chinese thought. FPA updates agents based on the generating and overcoming strategy as well as learning strategy from the agent with the same label. FPA has a simple structure but excellent performance. It also does not have any predefined control parameters, only two general parameters including population size and terminal condition are required. This provides flexibility to users to solve different optimization problems. For global optimization, 10 test functions from the CEC2019 test suite are used to evaluate the performance of FPA. The experimental results confirm that FPA is better than the 6 state-of-the-art algorithms including particle swarm optimization (PSO), grey wolf optimizer (GWO), multi-verse optimizer (MVO), differential evolution (DE), backtracking search algorithm (BSA), and slime mould algorithm (SMA). Furthermore, FPA is applied to solve the Economic Load Dispatch (ELD) from the real power system problem. The experiments give that the minimum cost of power system operation obtained by the proposed FPA is more competitive than the 14 counterparts. The source codes of this algorithm can be found in https://ww2.mathworks.cn/matlabcentral/fileexchange/118215-five-phases-algorithm-fpa.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

A multi-timescale optimal operation strategy for an integrated energy system considering integrated demand response and equipment response time

The response potential of demand-side resources is becoming increasingly significant in integrated energy system (IES) operations. In addition, to ensure the effective participation of system devices, their actual responsiveness at different timescales should be considered. Based on these considerations, this paper proposes an IES multi-timescale operation optimization strategy that incorporates multiple forms of integrated demand response (IDR) and considers the response characteristics of the equipment. First, the multi-timescale characteristics of IDR are analyzed. Moreover, a multi-timescale operation model of IES that comprises day-ahead, intraday, and real-time stages is further established. In the day-ahead dispatch, a low-carbon economic scheduling model is developed by considering the shifting demand response (DR) and the cost of carbon emissions. In the intraday scheduling, noting that cooling and heat energy transmission possess slow dynamic characteristics, a rolling optimization model for cooling/heating coupled equipment considering load shedding and substituting DR is established. In real-time scheduling, the output of electric/gas coupled equipment is adjusted. Finally, an industrial park-type IES in northern China was selected as an example for a case study. The results show that (1) the IDR multi-timescale response strategy can exploit different types of demand-side flexibility resources. After implementing the shifting DR, the peak-to-valley difference of the electric load curve was reduced by 20%, and the total system cost was reduced by 2.3%. After implementing load shedding, the maximum load differences per unit period of the electric, heat, and cooling load curves decreased by 18.7%, 40.0%, and 68.9%, respectively. (2) By refining the timescale of IES optimization, the proposed model can effectively ensure the energy supply and demand balance of the system under different load scenarios and reduce the system operation cost. After applying the model to simulation in three typical days (transition season, summer, and winter), the penalty costs of lost loads reduce by ¥3650, ¥3807, and ¥3599, respectively, and the total system costs decrease by 17.4%, 16.1%, and 16.2%, respectively.

Blockchain-Enabled Carbon and Energy Trading for Network-Constrained Coal Mines With Uncertainties

In this paper, a blockchain-enabled distributed market framework is proposed for the bi-level carbon and energy trading between coal mine integrated energy systems (CMIESs) and a virtual power plant (VPP) with network constraints. To maximize the profits of these two entities and describe their complicated interactions in the market, the bi-level trading problem is formulated as a Stackelberg game considering integrating the energy market and the “cap-and-trade” carbon market mechanism. Meanwhile, in the CMIES, energy recovery units and belt conveyors can be flexibly scheduled and the pumped hydroelectric storage in the VPP is scheduled for energy management. To tackle uncertainties from PV outputs, the joint trading, and the energy management is solved by the distributionally robust optimization (DRO) method. In addition, for participants' privacy, the alternating direction method of multipliers (ADMM) - based DRO algorithm is applied to solve the trading problem in a distributed framework. Further, the Proof-of-Authority (PoA) blockchain is deployed to develop a safe and anonymous market platform. Finally, case studies along with numerous comparison cases are conducted to verify the effectiveness of the proposed method. Simulation results indicate that the proposed method can effectively reduce the system operation cost and regional carbon emission, reduce the conservativeness and protect the privacy of each participant.

Transactive Energy for EV Owners and Aggregators: Mechanism and Algorithms

The increasing penetration of electric vehicles (EVs) brings new flexibility in power grid operation. As EVs serve the main purpose of transportation, EV owners will charge their vehicles based on their driving patterns. Given the clustering charging behavior, uncontrolled charging can burden the distribution grid, increasing system operation costs. As a distributed energy resource (DER) with a highly mobile and distributed nature, aggregated EVs also have the capabilities to provide ancillary services to the power grid to alleviate voltage violations and/or regulate frequency. This paper proposes a new bilateral trading and auctioning mechanism between aggregators and EVs. In this environment, EVs and aggregators can trade energy and ancillary services in a decentralized manner. The proposed EV transactive energy methodology for auctioning and clearing is based on the alternating direction method of multipliers (ADMM) optimization and blockchain technology. Simulation results for the proposed EV TRADing of Energy and Services (EV-TRADES) environment are provided using the NYISO market price data. Numerical results demonstrate the capability to coordinate EV charging and incentivize EVs to provide ancillary services.

Coordinated Optimal Power Dispatch Incorporating the Scheduling of Distributed Energy Resources Under the Virtual Power Plant Concept

In this paper, a method is proposed for coordinated optimal power dispatch (OPD) incorporating the scheduling of distributed energy resources (DERs (COPD-IDS). The proposed COPD-IDS aims to minimize the total daily operating cost of a power system by considering the optimal scheduling of DERs. In the problem formulation, the DERs are considered dispatchable limited energy units and treated as a virtual power plant (VPP). The OPD is solved for total hourly cost minimization, using quadratic programming (QP) as a subproblem in COPDIDS. Meanwhile, the total daily operating cost minimization incorporating the scheduling of DERs is solved by particle swarm optimization (PSO) and compared to a genetic algorithm (GA). The proposed COPD-IDS is tested on the modified IEEE 30-bus system under a practical load and the daily profiles of DERs. The simulation results show that the proposed method can minimize the total daily operational cost of the electricity system with the dispatchable condition of DERs using the VPP concept.

Nodal pricing of renewable-oriented distribution network in the presence of EV charging stations: A hybrid robust/stochastic optimization

The ever more increased usage of renewable resources and electric vehicles (EVs) with subsequent usage of charging stations (CSs) create new problems such as pricing and market clearing in their presence. Also, the possible uncertainties lead to huge problems in optimal operation of the distribution grid. The introduced framework in this paper addresses the possible uncertainties and risk conditions in the local market-clearing problem with the high penetration of CSs. The proposed model utilizes power tracing (PT) and loss allocation (LA) in its dynamic pricing scheme. Because of the CSs' role in the market and their different behavior with prosumers, the proposed bi-level model is a single leader and multi follower type. The distribution system operator (DSO) is the upper-level (leader) and the prosumer and CSs are each different lower-level (follower). The hybrid stochastic-robust local market clearing method is used in the proposed model. In the stochastic model, different scenarios for probabilistic parameters such as renewable energy sources (RES), loads, and electric vehicles (EVs) are generated to be used in the simulation. In the robust model, a wide range of errors from expected values are considered, which are used in the robust optimization model to solve the problem. The operation of CSs under stochastic-robust conditions and consideration of the EV owner's utility individually are investigated. The robust model's risk-averse conditions increase the network's prices and reduce electricity buying from the upstream grid. In order to hedge against the uncertainties, the distribution system operation costs only increases by 10.96 % for more 20 % robustness in optimal under-uncertainty operation.

Application of an intelligent method for hydrogen-based energy hub in multiple energy markets

Use of different energy carriers together, known as an energy hub, has been a hot topic of research. The hub manager manages the energy in the hub and supplies the energy demanded by the consumers. Herein, a multi-carrier energy storage system comprising a thermal energy storage system, ice storage system, and hydrogen storage system is considered for a multi-carrier energy system to exploit their economic benefits. The proposed combined cooling, heating and electricity microgrid not only participates in the electricity, gas and heat market to meet the needs of electricity, heating and cooling, but also can use the new hydrogen power technology used in the hydrogen storage system to increase the efficiency of the whole system participate in the market. In addition, a multi-energy demand response model is used as a new concept of demand response on electrical and thermal loads, which provides more options for multi-energy end users in energy management policies. To cover the uncertainty of the existence of different energy sources in the used model and the price of electricity, a new method has been used. The LHS sampling is used to develop diverse scenarios, and the backward scenario reduction method is adopted to decrease the number of scenarios. To solve the objective function and optimization, the metaheuristic whale algorithm improved by a local search algorithm is utilized. The main goal is to reduce the operation costs of the multi-energy carrier system. The obtained results show that the hydrogen storage utilization and multi-energy demand response can reduce the system operation cost by 6% for the studied experimental system. Furthermore, as the uncertainty budget increased, the total cost of operations increased by 10%. This is because the level of planning conservatism increases with each increase in the uncertainty budget.

Optimizing desalination for regional water systems: Integrating uncertainty, quality, and sustainability

Regional water supply systems in arid regions worldwide face growing challenges due to increasing demands under conditions of water scarcity. For coastal regions, the addition of seawater desalination as an alternative water source can hedge against uncertainty stemming from demands placed on the system and hydrological inputs to the natural resources. In order to effectively integrate desalination into a water supply system, water managers utilize optimization models that solve for the optimal mix of desalination and natural resources to meet consumer demands at a sufficient water quality while maintaining sustainable levels in the natural resources. The complexity of managing both quantity and quality for a water supply system facing uncertainties is increased by contractual limitations between desalination plant owners and water managers that dictate the desalination production decisions to occur at the start of the year before the uncertainties have been revealed. To incorporate the misalignment between decision timelines and the order in which the uncertainty is revealed, a two-stage optimization approach is utilized where management decisions are split into implementable decisions representing the desalination production volumes, and recourse decisions representing supply volumes from the remaining sources and water allocations in the supply network. An iterative process is then undertaken in which in the first stage the implementable decisions are solved for potential uncertain scenarios, and then introduced to the second stage as inputs where the recourse decisions are iteratively solved for new uncertain scenarios. This framework is applied to the Israeli national water supply system, where the unique challenges of managing mixed salinity sources and asynchronous decision timelines in the face of uncertainty are addressed. A multicriteria decision analysis method is then applied where tradeoffs are found between the upfront investment in desalination and the expected system operational cost and robustness to failure. The inclusion of multicriteria decision analysis to analyze the multiple desalination production alternatives allows for including stakeholders preferences in model-based decision-making.

On enhancing transmission performance for iov based on improved greedy algorithm

Nowadays, the development of 5G and IoT technology can be observed from abundance of their applications, such as VR/AR/MR, communication, healthcare, transportation and so on. Some of these applications are in need of quality improvement like delay and resource consumption decrease since the need of users and devices about the relatively developed technique gradually increase as well. About this kind of QoS improvement in the field of VANET, this paper first has a brief review on this concept and related key word and makes use of the concept of Edge Computing, eventually provides information about the research that focuses on the optimization of Task Offloading function by proposing a new algorithm. The new algorithm consists of 3 C++ programs and determines the relatively effective task offloading strategy by taking both calculating resource consumption and communication latency into consideration. In order to prove the developed performance of the proposed algorithm, paper uses other two kinds of algorithms to have comparison, finally reaches the conclusion that proposed algorithm has the best system cost performance among the comparison objects.

A techno‐economic model for enhancing PV hosting capacity through capabilities of smart inverters and soft open points

AbstractThe hosting capacity of solar photovoltaics (PVs) in the distribution network can be impacted by the violation of power flow constraints such as power loss, voltage, and line loadings. In this paper, a planning structure based on optimal PV placement and optimal operation of the smart inverters and soft open point (SOP) is proposed in order to investigate the loss of power, voltage profiles, line loadings and total operation cost of the distribution system. The stochastic nature of the daily load and PV generation is considered in power flow optimization by the proposed method. Respective objective functions are optimized using a genetic algorithm (GA) according to probabilistic power flow. While the desired objective function is optimized, the optimality of the PV locations and contribution of smart inverters and SOP is ensured through GA optimization. The proposed model is investigated through numerical analyses on IEEE 33‐bus and IEEE 69‐bus distribution systems considering different cases of reactive power supports. Developed GA is able to deal with challenges of convergence that are imposed by complex nonlinear formulations. Obtained results prove the superiority of the developed program for the investigation of the PV hosting capacity in both technical and cost aspects.

Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production

How to further increase the level of wind power consumption is a hot research topic in constructing new power systems around the world. It's a consensus that the electrolytic hydrogen production's participation in the power system can further improve the consumption of new energy. This paper proposes a new optimal scheduling strategy for the electro-thermal system, considering refined demand response and source-load-storage cooperative hydrogen production to increase the large-scale wind power consumption. Firstly, to alleviate the impact of wind power fluctuations on hydrogen production, a “wind-storage” combined hydrogen production module is proposed based on the characteristics of hydrogen energy to ensure the safe operation. Secondly, a refined demand response model is built to optimize the electric load curve through price and incentive guidance, to improve the flexibility of “source-load” coordination deeply. Then, to address the issue of wind power competition caused by benefits conflicts of system operation and hydrogen production, a Bi-level scheduling model of source-load-storage cooperative operation is established to achieve the maximum tracking of wind power, while ensure the interests of both sides. Simulation results show that the proposed strategy can balance the system operation cost and hydrogen production benefits, improve hydrogen revenue by 24%. And even if the capacity of hydrogen production device is low, 100% consumption of wind power can be achieved. This study is heuristic for the large-scale wind power consumption in the multi-energy coupled environment, and provides an idea for operation of new energy power system.

A Multicriteria Method to Form Optional Preventive Maintenance Plans: A Case Study of a Large Fleet of Vehicles

Motor vehicles are composed of a large number of parts, and planning the maintenance activities of different parts is a crucial decision that affects system reliability, operation costs, and capacity requirements of service providers. We propose a systematic method to determine the critical parts that should be handled with extra preventive maintenance (PM) and prepare alternative PM plans with different levels of cost and capacity usage. Our method uses a multicriteria decision-making approach to determine the critical parts and conducts statistical reliability analysis with failure data and expert knowledge to create the maintenance plans. We use the proposed method in a case study to determine optional PM packages that would support regular PM practices in the after-sales service of a large motor vehicle manufacturer. The main aim of the case study is to increase the satisfaction of customers who are more sensitive to failures, such as carriers of food and medical supplies. The results show that the optional PM packages can decrease the cost of failures while obeying the capacity limitation of the company.