Maximum Demand Charges Research Articles

Peak shaving in distribution networks using stationary energy storage systems: A Swiss case study

Grid operators are charged not only by their total energy demand, but also by their highest power demand from the superior grid level. The maximum demand charge is usually imposed on the peak power point of the monthly load profile, hence, shaving demand at peak times is of main concern for the aforesaid stakeholders. In this paper, we present an approach for peak shaving in a distribution grid using a battery energy storage. The developed algorithm is applied and tested with data from a real stationary battery installation at a Swiss utility. This paper proposes a battery storage control scheme that can be used for peak shaving of the total grid load under realistic conditions. Particularly, a rule-based approach combined with a deep-learning load forecasting model is developed and its performance is compared with the theoretical optimum based on real data from the field. The analysis includes both technical and economical results from a simulated storage operation and significant outcomes are given for the application of this method.

Energy cost minimization of a multifarious water heating system with energy recovery: A case of a healthcare institution

The energy usage intensity of healthcare institutions is extensive. The recorded relative typical energy consumption of hospitals has been noted to be in the range of 43 to 93 kWh/bed/day. Currently, with the global COVID 19 pandemic, the overall energy consumption of these hospitals has proportionally increased with these higher patient occupancy rates. The demand for hot water is directly proportional to occupancy rates and contributes to a large proportion of the energy consumed. Reducing the energy and associated costs of water heating processes may prove highly beneficial to the currently severely overstrained healthcare sector.In this paper the operation of a multifarious water heating system with energy recovery was optimized in order to minimize the grid energy costs based on the ToU tariff and maximum demand charges from the utility, while the required hot water temperature was maintained.A life cycle cost analysis between two baseline systems, consisting of the multifarious water heating system with and without energy recovery was compared to the optimal control approach. The results indicate that the energy recovery system with optimal control in 5.3 years. Retrospectively, the proposed initiatives may result in a potential saving of 68.23%, over a 20 year project lifetime.

A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions

Demand response with battery energy storage systems (BESS) provides the most flexible peak reduction solution for different markets. One of the major challenges is the optimization of the demand threshold that controls the charging and discharging powers of BESS. To increase its tolerance to day-ahead prediction errors, state-of-art controllers utilize rigid parameters that are determined from long-term historical data. However, long-term historical data may be unavailable at implementation, and rigid parameters cause them unable to adapt to evolving load patterns. To tackle this issue, this article proposes a novel dynamic two-stage maximum demand reduction controller using BESS that incorporates 1-h-ahead load profiles to refine the threshold found based on day-ahead load profile and prevent peak reduction failure if necessary. The dynamic controller needs no rigid parameters and can begin its daily peak reduction with just 30 days of historical data. Compared to the conventional fixed threshold, single-stage, and fuzzy controllers, the proposed two-stage controller achieves up to 6.82% and 306.23% higher in average maximum demand reduction and total maximum demand charge savings, respectively, on two different datasets. The proposed controller also achieves a 0% peak demand reduction failure rate in both datasets, demonstrating its peak demand reduction prevention capability.

Optimal energy management of a retrofitted Rubber Tyred Gantry Crane with energy recovery capabilities

In this work, an optimal energy management model for the grid-powered electric RTG, with a battery storage system, is developed. The aim of the model is to reduce the operation cost, by minimizing the component linked to the maximum demand charges from the grid, as well as the component linked to the Time of Use (ToU) pricing structure. As a case study, a RTG crane operating in South Africa has been selected. The load profile, size the battery storage system, ToU tariff, as well as the maximum demand charges, are used as input for the developed model. Simulations, for a complete RTG handling cycle, have been conducted to evaluate the techno-economic performances of the developed model, used to optimally dispatch the power flow in the system during the different phases of operation. Three main configurations have been simulated as energy sources for the RTG crane, namely, the exclusive supply from the grid, grid/battery hybrid system without energy recovery during the lowering phase and grid/battery hybrid with energy recovery, during the lowering phase.As compared to the baseline, the simulation results have shown that using the proposed model shows a possible 50.36, 60.6 and 64.4% cost reduction, per full handing cycle, are possible in off-peak standard and peak pricing period, for the selected winter day. The results also show that the maximum demand charges may be reduced by 45.20%.Further economic analyses have been conducted; and the results indicate that the proposed system successfully reduce electrical energy costs, the peak demand and outperforms each of the presented baselines.

Load Shifting and Peak Clipping for Reducing Energy Consumption in an Indian University Campus

This paper analyzes the intelligent use of time-varying electrical load via developing efficient energy utilization patterns using demand-side management (DSM) strategies. This approach helps distribution utilities decrease maximum demand and electrical energy billing costs. A case study of DSM implementation of electric energy utility for an educational building Alagappa Chettiar Government College of Engineering and Technology (ACGCET) campus was simulated. The new optimum energy load model was established for peak and off-peak periods from the system’s existing load profile using peak clipping and load shifting DSM techniques. The result reflects a significant reduction in maximum demand from 189 kW to 170 kW and a reduction in annual electricity billing cost from $11,340 to $10,200 (approximately 10%) in the upgraded system. This work highlights the importance of time of day (TOD) tariff structure consumers that aid reduction in their distribution system’s maximum demand and demand charges.

Rate design with distributed energy resources and electric vehicles: A Californian case study

The high penetration of distributed energy resources and electric vehicles is changing the way the electricity system is managed. In turn, the way utilities have been recovering their expenditures through tariffs needs reformulation. We investigate the impact of different retail tariff designs from a Californian scenario on private investment incentives and cost-shifting using solar PVs, stationary batteries, and electric vehicles. The commercial private facilities studied do not own the vehicles and the vehicle owners receive compensation for energy services provided, which strongly depends on the type of tariff applied. We found that energy-based tariffs with on-peak periods synchronized with solar PV production brought the highest private gains, but with high cost-shifting. On the other hand, the capacity-based tariffs reduced the economic benefits and cost-shifting concomitantly, mainly when on-peak periods defined by the rate matched the most constrained grid time window. Batteries are incentivized mostly to offset maximum demand charges rather than to arbitrage energy, but this will strongly depend on the spread between on-peak and off-peak periods. Coincident peak rates, coupled with EVs, can bring high remuneration for EV owners, second-highest net present value, and second-lowest cost-shifting among all rates. Finally, we derive policy implications from the results and earmark more sophisticated tariff designs for investigation.

Model validation and economic dispatch of a dual axis pv tracking system connected to energy storage with grid connection: A case of a healthcare institution in South Africa

Healthcare institutions, ranks the second highest energy intensive buildings in the commercial sector, with daily energy consumption ranging from 43 to 92 kWh per bed. Energy management in this sector may prove difficult due to the demand's critical and non-deferrable nature. Therefore, in this paper, a validated optimal power dispatch model of a proposed hybrid energy system connected to a healthcare institution is developed. The healthcare institution considered in this study, which will remain anonymous,11The healthcare institution considered in this paper is located in the Bloemfontein area and referred to as an anonymous healthcare entity in order to maintain confidentiality and privacy of sensitive data used in this study. is subjected to Time-of-Use (ToU) tariffs and maximum demand charges and is considering the implementation of renewable energy technologies to reduce energy costs. Consequently, given the geographical location of the building, a system consisting of a photovoltaic tracking system with a battery bank and grid connection supplying the load is proposed and modelled. The aim is to optimize the power flow between the hybrid system and battery in order to minimize the grid energy costs based on the ToU tariff while reducing the maximum demand charges from the utility. Annual potential cost savings of up to 40.9% were attained, while a projected break-even point of 7.1 years is noted with the proposed optimally controlled system as compared to the load exclusively supplied by the grid.

Water-Energy Management for Demand Charges and Energy Cost Optimization of a Pumping Stations System under a Renewable Virtual Power Plant Model

The effects of climate change seriously affect agriculture at different latitudes of the planet because periods of drought are intensifying and the availability of water for agricultural irrigation is reducing. In addition, the energy cost associated with pumping water has increased notably in recent years due to, among other reasons, the maximum demand charges that are applied annually according to the contracted demand in each facility. Therefore, very efficient management of both water resources and energy resources is required. This article proposes the integration of water-energy management in a virtual power plant (VPP) model for the optimization of energy costs and maximum demand charges. For the development of the model, a problem related to the optimal operation of electricity generation and demand resources arises, which is formulated as a nonlinear mixed-integer programming model (MINLP). The objective is to maximize the annual operating profit of the VPP. It is worth mentioning that the model is applied to a large irrigation system using real data on consumption and power generation, exclusively renewable. In addition, different scenarios are analyzed to evaluate the variability of the operating profit of the VPP with and without intraday demand management as well as the influence of the wholesale electricity market price on the model. In view of the results obtained, the model that integrates the management of the water-energy binomial increases the self-consumption of renewable energy and saves electricity supply costs.

A novel spontaneous self-adjusting controller of energy storage system for maximum demand reductions under penetration of photovoltaic system

Customers are subject to varying charges for their electricity consumption (kWh) as well as monthly maximum demands (kW) depending on the charging schemes for commercial and industrial customers. Generally, maximum demand charges may account for as high as 30% of the total electricity bills. Although on-site photovoltaic (PV) systems can help customers reduce their maximum demand charges, PV may not be as effective in reducing some of the peak demands due to the intermittent power output of PV. The inclusion of a battery-based energy storage system (BESS), on the other hand, can reduce those unexpected peaks by supplying power at the appropriate time and magnitude. Research efforts have therefore been carried out to develop control strategies for BESS to reduce the peak demands of PV customers. However, some of the existing controllers that rely on forecasted next-interval net demands to supply power for the next interval may fail to reduce peak demands effectively when the actual next-interval net demands are different from the forecasted ones. Hence, a spontaneous self-adjusting controller has been developed and presented in this paper to overcome this issue. It employs model predictive control and dynamic programming with anticipatory, preparatory and recovery actions to achieve a maximum demand reduction of at least 11.00% over the monthly maximum demand. Throughout an experimental peak reduction period of 4 months, the controller has also proven to achieve a reduction of at least 74.11% of the ideal reductions as compared to 68.00% and 65.00% reductions demonstrated by the preceding active and fuzzy controllers.

Techno-Economic Optimization of Grid-Connected Photovoltaic (PV) and Battery Systems Based on Maximum Demand Reduction (MDRed) Modelling in Malaysia

Under the present electricity tariff structure in Malaysia, electricity billing on a monthly basis for commercial and industrial consumers includes the net consumption charges together with maximum demand (MD) charges. The use of batteries in combination with photovoltaic (PV) systems is projected to become a viable solution for energy management, in terms of peak load shaving. Based on the latest studies, maximum demand (MD) reduction can be accomplished via a solar PV-battery system based on a few measures such as load pattern, techno-economic traits, and electricity scheme. Based on these measures, the Maximum Demand Reduction (MDRed) Model is developed as an optimization tool for the solar PV-battery system. This paper shows that energy savings on net consumption and maximum demand can be maximized via optimal sizing of the solar PV-battery system using the MATLAB genetic algorithm (GA) tool. GA optimization results revealed that the optimal sizing of solar PV-battery system gives monthly energy savings of up to 20% of net consumption via solar PV self-consumption, 3% of maximum demand (MD) via MD shaving and 2% of surplus power supplied to grid via net energy metering (NEM) in regards to Malaysian electricity tariff scheme and cost of the overall system.

Maximum Demand Management: An overlooked energy saving opportunity in industries

Maximum demand management is always overlooked as an energy saving opportunity in industry. Maximum demand charge is part of total electricity charges of an Industrial energy tariff and contributes around 20% of the total electrical energy bill. An energy audit conducted in an Industry revealed an opportunity of reducing the total plant maximum demand of average from 2736 kW to 2220 kW saving up to RM 17,000 on an average/month. The paper briefs the process re-engineering and technologies that could be used in the plant to reduce up to 20% Maximum demand charges from the total electricity bill. This can bring in a significant saving with minimal investment and is sustainable.

An Exploratory Factor Analysis (EFA) for Developing and Validating a Scale of Public Acceptance on Willingness to Pay (PAWP) Maximum Demand (MD) Charge in Malaysia

An Exploratory Factor Analysis (EFA) for Developing and Validating a Scale of Public Acceptance on Willingness to Pay (PAWP) Maximum Demand (MD) Charge in Malaysia

A Proposed Model to Determine Public Acceptance on Willingness to Pay Maximum Demand (MD) Charge in Malaysia

A Proposed Model to Determine Public Acceptance on Willingness to Pay Maximum Demand (MD) Charge in Malaysia

On the role of maximum demand charges in the presence of distributed generation resources

We examine the role that maximum demand charges (MDCs) might play in ensuring the financial viability of utilities in the presence of ever-expanding distributed generation (DG) of electricity. We find that optimally-designed MDCs generally secure gains for consumers that do not undertake DG, and often secure gains for consumers that undertake DG. However, the welfare gains tend to be modest in plausible settings. Furthermore, time-of-use pricing often secures larger welfare gains than do MDCs.

Grid-Tied Photovoltaic and Battery Storage Systems with Malaysian Electricity Tariff—A Review on Maximum Demand Shaving

Under the current energy sector framework of electricity tariff in Malaysia, commercial and industrial customers are required to pay the maximum demand (MD) charge apart from the net consumption charges every month. The maximum demand charge will contribute up to 20% of the electricity bill, and will hence result in commercial and industrial customers focussing on alternative energy supply to minimize the billing cost. This paper aims to review the technical assessment methods of a grid-connected solar photovoltaic (PV)—battery storage system—with respect to maximum demand shaving. An effective battery storage system can provide the extra energy needed during the peak energy consumption periods, as well as when renewable energy (RE) sources go offline. Based on the reviews, maximum demand shaving with good Return-of-Investment (ROI) can be achieved by considering the actual load profile, technical, and economic aspects of the solar PV-battery system and the Malaysian electricity tariff for commercial and industrial customers.

Active Control Strategy of Energy Storage System for Reducing Maximum Demand Charges under Limited Storage Capacity

AbstractCommercial and industrial customers are subject to monthly maximum demand charges, which can be as high as 30% of the total electricity bill. A battery-based energy storage system (BESS) ca...

A novel fuzzy control algorithm for reducing the peak demands using energy storage system

Commercial and industrial customers are subject to the monthly maximum demand charges which can be as high as 30% of the total electricity bills. Battery-based energy storage system (BESS) can be used to reduce the monthly maximum demand charges. A number of control strategies have been developed for the BESS to reduce the daily peak demands. A fuzzy control algorithm is developed to reduce the daily peak demands with the limited capacity of the BESS. Its performance is evaluated at two different buildings, namely building A and B. The fuzzy controller forecasts the load profile one day in advance using the historical load data. Then during the day of peak reduction, the controller will adjust the power output of BESS using the latest state of charge and operation time. The performance of the fuzzy controller is compared with other two controllers developed in the past. The experimental results show that fuzzy controller is the most effective approach for the peak reduction under the limited capacity of the BESS.

On the Role of Maximum Demand Charges in the Presence of Distributed Generation Resources

We examine the role that maximum demand charges (MDCs) can play in avoiding the death spiral that some utilities may otherwise face as the distributed generation (DG) of electricity proliferates. We find that MDCs generally secure gains for consumers that do not undertake DG, and often secure gains for consumers that undertake DG. However, the welfare gains tend to be modest in plausible settings. Furthermore, time-of-use pricing often secures larger welfare gains than do MDCs.

Techno-Economic Assessment of Redundancy Systems for a Cogeneration Plant

The use of distributed power generation has advantage as well as disadvantage. One of the disadvantages is that the plant requires a dependable redundancy system to provide back up of power during failure of its power generation equipment. This paper presents a study on techno-economic assessment of redundancy systems for a cogeneration plant. Three redundancy systems were investigated; using public utility, generator set and gas turbine as back up during failures. Results from the analysis indicate that using public utility provides technical as well as economic advantages in comparison to using generator set or turbine as back up. However, the economic advantage of the public utility depends on the frequency of failures the plant will experience as well on the maximum demand charge. From the break even analysis of the understudied plant, if the number of failures exceeds 3 failures per year for the case of maximum demand charge of RM56.80, it is more economical to install a generator set as redundancy. The study will be useful for the co-generator operators to evaluate the feasibility of redundancy systems.

Evaluation of Redundancy for Power Generating Unit in a Cogeneration Power Plant

Redundancy system is very useful to enhance the performance and reliability of the power generation system of a cogeneration plant. However, the associated operating cost of redundancy system is very high. The common redundancies used in a cogeneration power plant are public utility and generator set. In order to select the best redundancy options which incurs minimum operating cost, it is required to evaluate the cost of different redundancy options. In this paper, net present value model (NPV) is developed to evaluate the cost of redundancy considering availability and reliability of the cogeneration system. Two steps applied to evaluate the redundancy cost of the cogeneration system. The first step is predicting the number of failures and downtime hours using availability and reliability analysis because redundancy is frequently used when the system failed. The second step is evaluating the cost of redundancy using NPV model. The results indicate that the use of public utility as redundancy option is costly compared to generator set option for long period of time. The major operation cost of public utility is contributed by the maximum demand charge cost which is about 57.9% of the total cost of redundancy. The study will be useful as a guide for the cogeneration operation to evaluate and select the redundancy option.