Homogenising the Design Criteria of a Community Battery Energy Storage for Better Grid Integration

Historically, minimum system demand has usually occurred overnight. However, in recent years, the increased penetration of rooftop photovoltaic systems (RPVs) has caused an even lower demand at midday, forcing some of the conventional generators to shut down only hours before the evening peak demand period. This further complicates the job of power system operators, who need to run the conventional generator at the minimum stable level at the midday low-demand period so that they can reliably supply power during the peak periods. Employing a community battery storage system can alleviate some of the technical issues caused by the high penetration of RPVs. This paper proposed a design criterion for community battery energy storage systems and employed the battery for the improvement of the duck curve profile and providing the desired level of peak-shaving. Furthermore, remote communities with high penetration of RPVs with a community battery energy storage can achieve the desired level of self-sufficiency. To this end, this study recommends and confirms an applicable design criterion for community battery energy storage. The study shows that the suitable size of community battery storage should be based on the community’s daily excess generation and consumption requirements. The results of various scenarios performed on the proposed design criterion show the extent to which the desired objectives of peak-shaving, duck curve mitigation, and self-sufficiency can be achieved.

A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data

Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)—battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited.

Design and feasibility verification of regenerative system of electric thermal storage boiler for peak shaving in summer in power plant

During the heating period, the thermal storage electric boiler helps the thermal power units to participate in the deep peak regulation by converting the electric energy into heat energy for heating users, but in the non-heating period, the thermal storage electric boiler can not operate because there is no heat user, as a result, the thermal storage electric boiler is shut down in summer, and can not assist the thermal power unit to participate in the deep peak regulation. Therefore, this paper designs an electric thermal storage boiler regenerative system for peak shaving in summer. In this regenerative system, electric boiler is used to heat circulating water in heating period, and electric boiler is used to heat condensed water in non-heating period, and in the non-heating period, the number of low-pressure heaters can be adjusted according to the load and heat storage capacity of the units, so that the electric boiler can assist the thermal power units to participate in the deep peak regulation throughout the year, taking a 350MW unit with 70MW regenerative electric boiler as an example, the heat exchange capacity is calculated to verify the feasibility of the regenerative system. In this paper, a new method of heating condensate by regenerative electric boiler in non-heating period is proposed to solve the problem that the new energy can not be used and the energy is wasted in summer.

The Integrated Kinetic Energy Recoup Drive (i-KERD): An Optimized Powertrain for EVs, HEVs and FCEVs

In this paper, a particular form of flywheel hybrid powertrain, namely, the Integrated Kinetic Energy Recoup Drive (i-KERD) is fully explored and its applications for EVs, HEVs and FCEVs in recent years to show the energy-savings and performance enhancement potential of this innovative powertrain technology. It is shown that the i-KERD is a small highspeed flywheel integrated into an e-CVT, or power-split hybrid drive. Under NEDC or WLTC, typically it can achieve some 40% energy savings and >50% gain in 0–100 kph acceleration due to effective regenerative braking mechanism of the integrated flywheel power system. In addition to its “peak-shaving” capability, the highly-efficient, long-life flywheel power on-board, is able to keep the kinetic energy of the vehicle fully recycled, rather than dissipated during braking. The i-KERD technology has also been applied to urban railway transportation (i.e., underground railway) and off-road heavy construction equipment, where regenerative braking plays a great role on energy efficiency.

IoTivity Cloud-Enabled Platform for Energy Management Applications

In developing countries today, population growth and the penetration of higher standard of living appliances in homes has resulted in a rapidly increasing residential load. In South Africa, the recent rolling blackouts and electricity price increase only highlighted this reality, calling for sustainable measures to reduce overall consumption and peak load. The dawn of the smart grid concept, embedded systems, and ICTs have paved the way for novel Home Energy Management Systems (HEMS) design. In this regard, the Internet of Things (IoT), an enabler for intelligent and efficient energy management systems, is the subject of increasing attention for optimizing HEMS design and mitigating its deployment cost constraints. In this work, we propose an IoT platform for residential energy management applications focusing on interoperability, low cost, technology availability, and scalability. We addressed the backend complexities of IoT Home Area Networks (HAN) using the Open Consortium Foundation (OCF) IoTivity-Lite middleware. To augment the quality, servicing, reduce the cost, and the development complexities, this work leverages open-source cloud technologies from Back4App as Backend-as-a-Service (BaaS) to provide consumers and utilities with a data communication platform within an experimental study illustrating time and space agnostic “mind-changing” energy feedback, Demand Response Management (DRM) under a peak shaving algorithm yielded peak load reduction around 15% of the based load, and appliance operation control using a HEM App via an Android smartphone.

Peak Shaving and Frequency Regulation Coordinated Output Optimization Based on Improving Economy of Energy Storage

In this paper, a peak shaving and frequency regulation coordinated output strategy based on the existing energy storage is proposed to improve the economic problem of energy storage development and increase the economic benefits of energy storage in industrial parks. In the proposed strategy, the profit and cost models of peak shaving and frequency regulation are first established. Second, the benefits brought by the output of energy storage, degradation cost and operation and maintenance costs are considered to establish an economic optimization model, which is used to realize the division of peak shaving and frequency regulation capacity of energy storage based on peak shaving and frequency regulation output optimization. Finally, the intra-day model predictive control method is employed for rolling optimization. An intra-day peak shaving and frequency regulation coordinated output optimization strategy of energy storage is proposed. Through the example simulation, the experiment results show that the electricity cost of the whole day is reduced by 10.96% by using the coordinated output strategy of peak shaving and frequency regulation. The obtained further comparative analysis results and the life cycle economic analysis show that the profit brought by the proposed coordinated output optimization strategy is greater than that for separate peak shaving or frequency modulation of energy storage under the same capacity.

Design and simulation analysis of high-temperature heat-storage combined-circulation system

In view of the continuous increase in the proportion of renewable energy connected to the grid in China and the increasing peak-to-valley difference in electricity demand on the power grid, this paper proposes a high-temperature thermal-storage combined-cycle power-generation system. Using Thermoflex thermal simulation analysis software, a high-temperature thermal-storage combined-cycle simulation analysis system model was established, and the influence of different initial temperatures and pressure ratios on the combined-cycle system was analysed. Sensitivity analysis of factors such as electricity cost, annual operating hours, initial equipment investment, unit efficiency and other factors that affect the net income of the system was carried out. According to the current power-peak-shaving auxiliary service market in China, it is pointed out that high-temperature thermal-storage combined-cycle projects must be profitable and obtain good economic benefits. The results obtained, on the one hand, provide suggestions for the flexibility and transformation of current gas-fired steam generators for peak shaving and, on the other hand, provide references for the subsequent development of high-temperature thermal-storage combined-cycle demonstration projects.

Multi-Household Energy Management in a Smart Neighborhood in the Presence of Uncertainties and Electric Vehicles

The pathway toward the reduction of greenhouse gas emissions is dependent upon increasing Renewable Energy Sources (RESs), demand response, and electrification of public and private transportation. Energy management techniques are necessary to coordinate the operation in this complex scenario, and in recent years several works have appeared in the literature on this topic. This paper presents a study on multi-household energy management for Smart Neighborhoods integrating RESs and electric vehicles participating in Vehicle-to-Home (V2H) and Vehicle-to-Neighborhood (V2N) programs. The Smart Neighborhood comprises multiple households, a parking lot with public charging stations, and an aggregator that coordinates energy transactions using a Multi-Household Energy Manager (MH-EM). The MH-EM jointly maximizes the profits of the aggregator and the households by using the augmented ϵ-constraint approach. The generated Pareto optimal solutions allow for different decision policies to balance the aggregator’s and households’ profits, prioritizing one of them or the RES energy usage within the Smart Neighborhood. The experiments have been conducted over an entire year considering uncertainties related to the energy price, electric vehicles usage, energy production of RESs, and energy demand of the households. The results show that the MH-EM optimizes the Smart Neighborhood operation and that the solution that maximizes the RES energy usage provides the greatest benefits also in terms of peak-shaving and valley-filling capability of the energy demand.