Contract Capacity Research Articles

Contracted Capacity Optimization Problem of Industrial Customers with Risk Assessment

This study developed a risk assessment tool for contract capacity optimization problems using the ant colony optimization and auto-regression model. Based on the historical data of demand consumption, the Least Square algorithm, the Recursive Levinson–Durbin algorithm, and the Burg algorithm were used to derive the auto-regression model. Then, ant colony optimization was used to search for the auto-regression model’s best p-order parameters. To avoid the risk of setting the contract capacity, this paper designed the risk tolerance parameter β to correct the predicted value of the auto-regression model. Ant colony optimization was also used to search for the optimal contract capacity with risk assessment under the two-stage time-of-use and three-stage time-of-use. This study employed an industrial consumer with high voltage power in Taiwan as the research object, used the AR model to estimate the contract capacity under the risk assessment, and cut back electricity usage to reduce the operation cost. The results can be used as a basis to develop an efficient tool for industrial customers to select contract capacities with risks to obtain the best economic benefits.

In-depth Study on RTT-HC-MTT Relationship for Passenger Demand beyond Elevator Contract Capacity by Simulation

The traditional elevator system design practice is to calculate the round trip time (RTT) and associated parameters of pure incoming traffic during up-peak, followed by real-time computer simulation. Recent studies indicated that the normal traffic is much more complicated, consisting of a mixture of incoming, outgoing and interfloor patterns. The Universal RTT, under such complicated traffic patterns, was analytically developed eight years ago based on the concept of an appropriate origindestination matrix describing the passenger transit probability, and verified by Monte Carlo simulation. That model is based on the assumption that the total number of passengers demanding service within one round trip is limited to the elevator contract capacity, which is in line with the traditional up-peak incoming RTT formula. The idea of extending the consideration to beyond the contract capacity was initiated two years ago. In this article, an in-depth study on such consideration is carried out so that the performance such as RTT, handling capacity (HC) and mean transit time (MTT) etc. under different traffic patterns is evaluated and analyzed with the help of Monte Carlo simulation. This article may help designers optimally size an elevator system during the RTT calculation stage without oversizing it if the prevalent traffic patterns of the building are known.

Unhedged risk in hybrid energy markets: Optimising the revenue mix of Australian solar

Australia is facing a historic investment challenge, with an estimated $1.2 to $1.5 trillion in newly invested capital required by 2030 in order to meet 2050 decarbonisation targets. In response policy makers have begun “re-entering” energy markets, which were deregulated and privatised in the 1990’s. Public auctions for State-backed ‘Contracts-for-Differences’ (CfDs) now act as a key policy tool to “prime” energy markets for new variable renewable energy (VRE) capacity. In a phenomenon recently coined “market hybridisation”, market participants form long-term investment decisions that are increasingly disconnected from short-run spot pricing dynamics and day-to-day operations. Historically, it has been common practice to underpin VRE projects with some form of power purchase agreement (PPA). However, increasing investor appetites for “semi-merchant” projects (i.e. projects partially exposed to spot prices) has become an emerging trend. This article examines the semi-merchant phenomenon, aiming to explain why the investment model is becoming popular for investors in VRE. Of particular interest are the foregone advantages attributable to highly contracted plant when securing bankable project finance. Results reveal that a revenue mix comprising 70%–80% PPA coverage and 20%–30% merchant exposure appears viable and tractable for investors in utility scale solar — whilst maintaining a typical project finance capital structure of c.60%–70% gearing. By implication, policymakers targeting 4000MW of new solar capacity via CfD auction may need only offer ∼3000MW of contracted capacity, thereby reducing taxpayer exposures and protecting scarce government balance sheet resources.

Capacity planning with uncertainty on contract fulfillment

This paper focuses on the tactical planning problem faced by a shipper which seeks to secure transportation and warehousing capacity, such as containers, vehicles or space in a warehouse, of different sizes, costs, and characteristics, from a carrier or logistics provider, while facing different sources of uncertainty. The uncertainty can be related to the loads to be transported or stored, the cost and availability of ad-hoc capacity on the spot market in the future, and the availability of the contracted capacity in the future when the shipper needs it. This last source of uncertainty on the capacity loss on the contracted capacity is particularly important in both long-haul transportation and urban distribution applications, but no optimization methodology has been proposed so far. We introduce the Stochastic Variable Cost and Size Bin Packing with Capacity Loss problem and model that directly address this issue, together with a metaheuristic to efficiently address it. We perform a set of extensive numerical experiments on instances related to long-haul transportation and urban distribution contexts and derive managerial insights on how such capacity planning should be performed.

ESS Scheduling and Control Approach for Factory-based EV Charging Station to Participate in Ancillary Services

Due to the global trend towards net zero emissions and increasing environmental awareness, some renewable energy sources (RES) and electric vehicles (EVs) have been integrated into the power system. An energy management system (EMS) can effectively integrate and coordinate distributed energy resource (DER) control in response to grid changes and impacts. Proper regulation of renewable energy and electric vehicles can reduce microgrid operating costs, which are critical for improving the safety and stability of power systems. Therefore, this paper proposes a three-stage optimal scheduling strategy from the perspective of a microgrid operator (MGO) to efficiently utilize resources. Based on an electric energy storage system (ESS), the traditional centralized optimal control method is compared with the method proposed to calculate the time and operating cost. It is verified that the method proposed can indeed execute an ancillary service (AS). In addition, the model predictive control (MPC) employed by the ESS can significantly improve the robustness of the system and reduce the occurrence of exceeding contract capacity.

How can procurement create (sustainable) public value under the Bipartisan Infrastructure Deal?

AbstractThe economic response of the US government to the COVID‐19 pandemic envisions massive investment in infrastructure construction. Yet, governments contract out public works and might lack the capacity to meet the increased demand for new construction. Drawing on a mix of survey and interview data, we identify critical deficiencies in contract capacity that might lead to a loss of public resources and further erode trust in the government. We propose a plan for restructuring public procurement systems and offer solutions around four foci: collaboration, training, flexibility, and sustainability. This transformation path would enhance government contract capacity and use markets to signal a demand for sustainable infrastructure and create public value in line with the strategic objectives of the Bipartisan Infrastructure Deal.

Application of the artificial bee colony algorithm to power contract capacity optimization

In recent years, the artificial intelligence of imitation human neuron operation has become an aid to human thinking. Therefore, we applied this technique to forecast medium-term load systems. Power loads represent an indispensable energy in our life, so effectively forecasting power load usage may not only decrease energy consumption but also reduce the cost of contract capacity. Currently, people have used end-use models, load density, trend analysis, and statistical methods to predict Medium-Term Load Forecasts (MTLF) and have employed artificial neural networks to predict Short-Term Load Forecasts (STLF). In this paper we propose an optimized structure to predict the power load by combining an artificial bee colony (ABC) and an artificial neural network (ANN) to forecast MTLF. After considerable trial-and-error testing, the influence of different structures, max iteration, and datasets leading to different errors all became points in our study. These structures were also put into both a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) to test the experiment. The experimental results verified that ABC demonstrated better performance in max iteration 30 and structure 7-n-1. An appropriate contract capacity prediction can be obtained by applying this structure. To show the effectiveness of the proposed structure, we verified it with six years’ statistical data of the health care service industry from Taiwan Power Company and then discuss the results.

A bottom-up approach for techno-economic analysis of battery energy storage system for Irish grid DS3 service provision

This paper presents a bottom-up approach for techno-economic analysis of a Li-ion battery based Energy Storage System (BESS) to provide grid ancillary services under the “Delivering a Secure, Sustainable Electricity System” (DS3) programme in the Irish market. There are two types of service procurement mechanisms that exist in the DS3 programme: volume capped and volume uncapped. A design methodology of the storage system is investigated to optimise the installed capacity and minimize the initial cost for volume capped DS3 services. Based on the real-life project assessment, three different cost models are considered to evaluate different investment indices. The goal is to assess the economic performance of BESS for different contracted capacity systems under the different operating and financial conditions. Extensive simulation and sensitivity analysis based on the Monte Carlo method have been carried out to compare the cost model outputs. The findings are expected to give a clear idea to the investors and decision-makers about the Irish DS3 ancillary service market and clarify specific concerns on the economic feasibility of BESS for DS3 services. Moreover, it discusses the risks and opportunities related to the high volatility of the system component costs. • A bottom-up approach for Techno-economic analysis of ESS to provide DS3 grid services. • A simplified optimal sizing method for a Volume Capped ESS-based DS3 providing unit. • Clarify specific concerns on the economic feasibility of ESS for DS3 Services. • Analyse the solution economic sensitivity towards equipment cost variations. • Provide a comprehensive perception to stakeholders into the Irish DS3 service market.

Maximizing public welfare for the net-metering scheme using prosumer charges – vertically integrated utility case

Recent cost development of Photovoltaic system has pushed for increasing penetration of distributed roof-top application. This increased penetration has been supported by different policy schemes. This paper proposes an approach to quantify the impact of the net-metering scheme in addition to a new implementation strategy of net-metering that balances public welfare, utility cost recovery and renewable energy targets. It uses the GAMS and MATLAB software in modelling all actors including consumers, prosumers and the utility itself. It further proposes a mechanism to increase the public welfare in the electricity sector to accommodate solar PV investments driven by net-metering scheme. The proposed public welfare-based approach takes into consideration maintaining a balance between maximizing the solar PV investments while guaranteeing cost recovery of vertically integrated utilities. A model is developed to investigate the performance of prosumer charges in maintaining the required balance. Results show that assigning prosumer charges using the proposed model will have minimum impact on tariff hikes. In addition, the modelling results confirms the similarity of the performance of both implementation of prosumer charges either based on generated energy or contracted capacity and their superior performance compared to the one based on observed capacity. It further recommends increasing the efficiency of the prosumer charges on two levels. The first is to differentiate it based on location as to integrate the network benefits namely reducing losses at the distribution level. The second is to complement it with a monetized certificate of origin that values the environmental value of the net-metered PV energy.

A study on universal round trip time analysis for passenger demand beyond elevator contract capacity by Monte Carlo simulation

The traditional elevator system design practice is to calculate the round trip time (RTT) and associated parameters of pure incoming traffic during up-peak, followed by real-time computer simulation. Recent studies indicated that the normal traffic is much more complicated, consisting of a mixture of incoming, outgoing and interfloor patterns. A major breakthrough to analytically calculate the Universal RTT, under such complicated traffic patterns, emerged 6 years ago based on an appropriate origin-destination matrix describing the passenger transit probability. That genesis model played safe by assuming that the total number of passengers demanding service within one round trip is limited elevator contract capacity, which is in line with the traditional up-peak incoming RTT formulae. In this article, such assumption is removed and the study is based on Monte Carlo simulation. It is found that there is room for enhancing the handling capacity, up to two times the contract capacity, by not sacrificing the RTT and average passenger transit time by too much. This phenomenon, that is, total passenger demand beyond contract capacity, is only valid under the existence of multiple entrance floors and/or mixed traffic conditions. This approach may prevent oversizing the design which could be more realistic. Practical applications: Elevator system designers, according to ISO 8100:32:2020 and CIBSE Guide D: 2020, are recommended to carry out calculation of the RTT and related parameters before any real-time computer simulation. This practice has been adopted by the elevator industry for decades. However, conventional RTT evaluation is mainly on pure incoming traffic during up-peak. The Universal RTT calculation method developed in 2014–15 extended RTT evaluation to cover dominant and complicated traffic patterns of modern buildings, but the assumed number of passengers to be handled within one round trip was limited to the contract capacity of the elevator. This article further removes this limitation to evaluate the limit of handling capacity with reasonable RTT and average passenger transit time. Then, the Universal RTT method could be more realistic and rolled out, and prevent oversizing the system design.

A Study on Improving Economy Efficiency of Pumping Stations Based on Tariff Changes

In this paper, research on improving the economic efficiency of 38 drainage pumping stations was undertaken. Particular attention was paid to the effectiveness of activities without considering any expenditures. Energy costs for this type of machine are usually high, approximately 45% of the total maintenance cost. The main assumption of this work was the selection of appropriate energy tariffs to reduce operating costs. Liquid transport in any economy consumes significant amounts of electricity, estimated at 20–30% of the total electricity production. The optimization of the energy consumption of pumping processes is, therefore, very important. While analyzing the choice of energy tariffs, we designed profitability ratios (PR) specifically for different daily time zones. With the forecasted distribution of energy demands for different daily time zones (usually 2 or 3 zones), it was possible to compare multi-zone tariffs with 24 h tariffs. The profitability of the tariffs was decided by the value of the PR indicator. The lower, the better. In practice, this meant that the analyzed multi-zone tariffs, in most cases, are more profitable compared to single-zone tariffs. In the Polish energy system, each entrepreneur, depending on the connection power, has a right to choose a particular energy tariff from three tariff groups, i.e., high (A), medium (B), and low (C) voltage. In the case of land reclamation pumping stations, energy tariffs are usually from groups B and C. The choice of tariffs largely depends on the contracted capacity and is determined by tariff regulation. Nowadays, the current energy system forces entrepreneurs to declare a connected power load at the level corresponding to the maximum use of the production potential. Lack of knowledge of the hydrological regime, quite common for land reclamation pumping stations, usually results in overestimating the contracted capacity. When comparing the effect of changing tariffs, it was found that the profitability of this method is significant. The four-year study period (2010–2013) showed that active energy in the multi-part tariffs of groups B and C is on average 10.2% cheaper than in the single-part tariffs and varies in a wide range from 2% to 20.4%. The analysis carried out on 38 drainage pumping stations shows that for only five pumping stations could changing the current tariff be unjustified. In the other cases, the four years of analysis demonstrates that changing the current energy tariff could reduce electricity costs by approximately 5%., i.e., approximately PLN 124,000 per year (approximately EUR 27,000).

Forecasting Method of Newly-Connected Customer&s Load Curve on LV distribution by Means of Regression Tree Utilizing Smart Meter Data

Maximum current is necessary to be estimated when designing a LV distribution grid, i.e. MV/LV pole-mounted transformers (hereinafter referred to as Pole-Tr) and LV feeders, which supplies customers of several in minimum and dozens in maximum. This is currently estimated from the total contract capacities of the customers supplied by the grid, multiplying diversity and demand factor corresponding to the number of customers. This method is rather efficient and simple because calculations can be done simply by using contract capacities. However, such calculations may not be correct as they were derived statistically from sampled current measurements of customers. Therefore, we analyzed the SM data collected for each customer attribute information and calculated the representative profile. Machine learning is performed using the calculated representative profile as teacher data to generate a regression tree. Using the generated regression trees, we have constructed a proposal method for assuming the maximum power of the Pole-Tr to be connected together with the load curve of the new customer from the customer attribute information obtained when the customer applied for electricity use. This makes it possible to design LV distribution with higher accuracy than the conventional method.

Optimal Configuration with Capacity Analysis of PV-Plus-BESS for Behind-the-Meter Application

As the cost of photovoltaic (PV) systems and battery energy storage systems (BESS) decreases, PV-plus-BESS applied to behind-the-meter (BTM) market has grown rapidly in recent years. With user time of use rates (TOU) for charging and discharging schedule, it can effectively reduce the electricity expense of users. This research uses the contract capacity of an actual industrial user of 7.5 MW as a research case, and simulates a PV/BESS techno-economic scheme through the HOMER Grid software. Under the condition that the electricity demand is met and the PV power generation is fully used, the aim is to find the most economical PV/BESS capacity allocation and optimal contract capacity scheme. According to the load demand and the electricity price, the analysis shows that the PV system capacity is 8.25 MWp, the BESS capacity is 1.25 MW/3.195 MWh, and the contract capacity can be reduced to 6 MW. The benefits for the economical solution are compared as follows: 20-year project benefit, levelized cost of energy (LCOE), the net present cost (NPC), the internal rate of return (IRR), the return on investment (ROI), discounted payback, total electricity savings, renewable fraction (RF), and the excess electricity fraction. Finally, the sensitivity analysis of the global horizontal irradiation, electricity price, key component cost, and real interest rate will be carried out with the most economical solution by analyzing the impacts and evaluating the economic evaluation indicators. The analysis method of this research can be applied to other utility users to program the economic benefit evaluation of PV/BESS, especially an example for Taiwan’s electricity prices at low levels in the world.

Power Conversion System Operation to Reduce the Electricity Purchasing Cost of Energy Storage Systems

A strategy to operate a power conversion system (PCS) to minimize the electricity rate of an energy storage system (ESS) is formulated. The ESS operation method is determined considering the power management system (PMS). The primary functions include peak-cut, peak-shifting, and frequency regulation typically related to electricity rates. Thus, the battery is charged and discharged when the price is low and high, respectively, thereby monetizing the battery. However, the ESS incurs a high cost for the batteries and PCS. Therefore, ESSs that reuse electric vehicle (EV) batteries are being actively developed. Many researchers have attempted to maximize the profit of ESSs by developing algorithms to calculate the optimal ESS capacity by performing a power load analysis of electricity consumers. An ESS selected based on this calculation can be operated through the PMS. This ESS can use the battery state of charge (SoC), ranging from 10–90%, to conduct a feasibility analysis using the net present value, which reflects the current electricity rate. This feasibility analysis is performed considering the difference between the initial investment cost of the ESS and the profit obtained from the power generation of the ESS. In South Korea, many policies have been implemented to encourage the installation of ESSs. The ESS promotion policy was implemented until 2020 to reduce the electricity rate, including the contracted capacity of batteries. However, since 2021, this policy has been transformed to reduce the electricity rate based on the daily maximum power generation. Thus, the conventional method of increasing the battery capacity is not suitable, and the profitability should be increased using limited batteries. For ESSs, PCSs composed of single and parallel structures can be used. When installing a large capacity ESS, a PCS using silicon (Si) is adopted to reduce the unit cost of the PCS. The unit price of a silicon carbide (SiC) device has recently decreased significantly. Thus, in this study, a PCS using this SiC device was developed. Moreover, an algorithm was formulated to minimize the electricity rate of the ESS, and the operation of a modular type PCS based on this algorithm was demonstrated.

Integration of multicriteria decision analysis and geographic information system for site suitability assessment of Napier grass-based biogas power plant in southern Thailand

Napier grass is a promising energy crop owing to its advantages over other energy crops; however, when determining sites for the power plants utilizing Napier grass as a feedstock, relevant data are required. The spatial distribution of the Napier grass dry matter yield (DMY) is among these important data. Unlike agricultural residues, the spatial distribution of the DMY cannot be obtained through surveys or statistical data. In this study, a methodology for locating biogas power plants utilizing Napier grass as a feedstock is proposed. A geographic information system and analytic hierarchy process are used to evaluate land suitability for biogas power plants based on environmental and socioeconomic criteria. The spatial distribution of the DMY is integrated with the land suitability map to locate suitable sites for biogas power plants. The proposed approach is applied to southern Thailand because the region lacks self-sufficient power generation. The results reveal that, five biogas power plants that utilize Napier grass from abandoned areas can be established with a total contracted capacity of 420 MW. Based on this analysis, introducing Napier grass-based biogas power plants could significantly help reduce the dependency on imported electricity. Stakeholders are recommended to treat this study as a tool to be utilized before implementing new dedicated energy crops.

Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.

An energy management strategy with renewable energy and energy storage system for a large electric vehicle charging station

Abstract With the increase in the use of electric vehicles, charging stations may have congestion problems. The grid energy storage system can be used to satisfy the energy demand for charging electric vehicles batteries. Electric vehicles charging/discharging scheduling for vehicle-to-grid and grid-to-vehicle operations is challenging because customers have different energy requirements. Here, a charging and discharging power scheduling algorithm solved by a chance constrained programming method was applied to an electric vehicle charging station which contains maximal 500 charging piles, an 100kW/500 kWh energy storage system, and a 400 kWp photovoltaic system. Accordingly, the power dispatch can be beneficial to the charging station and electric vehicle users under the possible impact of the generation uncertainty of PV. The time-of-use adjustment method is proposed integrated with the charging/discharging priorities calculation and electricity prices, which ensures the energy usage does not exceed contract capacity. Based on the proposed algorithm, a blueprint for optimizing the contract capacity is analyzed for improving the cost of charging stations. The results indicate that the cost of electric vehicles charging can be decreased almost by 50% in a certain confidence level of photovoltaic forecasting comparing with uncoordinated method. The proposed method can assist charging station operators to evaluate suitable contract capacity and implement power dispatch strategies based on the possible scale of electric vehicles and distributed energy resources, which is conducive to the development of the power and EV industry.

Asymmetric Loss Functions for Contract Capacity Optimization

For high-voltage and extra-high-voltage consumers, the electricity cost depends not only on the power consumed but also on the contract capacity. For the same amount of power consumed, the smaller the difference between the contract capacity and the power consumed, the smaller the electricity cost. Thus, predicting the future power demand for setting the contract capacity is of great economic interest. In the literature, most works predict the future power demand based on a symmetric loss function, such as mean squared error. However, the electricity pricing structure is asymmetric to the under- and overestimation of the actual power demand. In this work, we proposed several loss functions derived from the asymmetric electricity pricing structure. We experimented with the Long Short-Term Memory neural network with these loss functions using a real dataset from a large manufacturing company in the electronics industry in Taiwan. The results show that the proposed asymmetric loss functions outperform the commonly used symmetric loss function, with a saving on the electricity cost ranging from 0.88% to 2.42%.

An enhanced stochastic algorithm for optimal contract capacity of furniture stores

This paper integrated the Grey Theory and Particle Swarm Optimization to propose an Enhanced Stochastic Algorithm (ESA) for dealing with the optimal contact capacity of the furniture store. ESA is used to find the minimal total annual electricity bill and simultaneously find the optimal contract capacity under the operational and system’s constraints. The historical demand for a furniture store is first collected in this paper. Grey theory is used to predict the next year demand of every month. Based on the Time-Of-Use (TOU) rates employed by the Taiwan Power Company (TPC), the annual electric bill including basic charge and penalty charge is derived to formulate an objective function. Particle Swarm Optimization (PSO) was adopted to minimize the annual electricity bill and simultaneously find the optimal contract capacity. Practical load consumption data were used to prove the validity of the ESA. Users can able to estimate the contract capacity by themselves and curtail the electricity cost.

Implementation of a Demand-Side Management Solution for South Korea’s Demand Response Program

In this paper, we show the development of a demand-side management solution (DSMS) for demand response (DR) aggregator and actual demand response operation cases in South Korea. To show an experience, Korea’s demand response market outline, functions of DSMS, real contracted capacity, and payment between consumer and load aggregator and DR operation cases are revealed. The DSMS computes the customer baseline load (CBL), relative root mean squared error (RRMSE), and payments of the customers in real time. The case of 10 MW contracted customers shows 108.03% delivery rate and a benefit of 854,900,394 KRW for two years. The results illustrate that an integrated demand-side management solution contributes by participating in a DR market and gives a benefit and satisfaction to the consumer.