Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System

Stochastic interval-based optimal offering model for residential energy management systems by household owners

A data-driven method to characterize turbulence-caused uncertainty in wind power generation

Australia has clear aspirations to become a major global exporter of hydrogen as a replacement for fossil fuels and as part of the drive to reduce CO2 emissions, as set out in the National Hydrogen Strategy released in 2019 jointly by the federal and state governments. In 2021, the Australian Energy Market Operator specified a grid forecast scenario for the first time entitled “hydrogen superpower”. Not only does Australia hope to capitalise on the emerging demand for zero-carbon hydrogen in places like Japan and South Korea by establishing a new export industry, but it also needs to mitigate the built-in carbon risk of its export revenue from coal and LNG as major customers, such as Japan and South Korea, move to decarbonise their energy systems. This places hydrogen at the nexus of energy, climate change mitigation and economic growth, with implications for energy security. Much of the published literature on this topic concentrates on the details of what being a major hydrogen exporter will look like and what steps will need to be taken to achieve it. However, there appears to be a gap in the study of the implications for Australia’s domestic energy system in terms of energy security and export economic vulnerability. The objective of this paper is to develop a conceptual framework for the implications of becoming a major hydrogen exporter on Australia’s energy system. Various green hydrogen export scenarios for Australia were compared, and the most recent and comprehensive was selected as the basis for further examination for domestic energy system impacts. In this scenario, 248.5 GW of new renewable electricity generation capacity was estimated to be required by 2050 to produce the additional 867 TWh required for an electrolyser output of 2088 PJ of green hydrogen for export, which will comprise 55.9% of Australia’s total electricity demand at that time. The characteristics of comparative export-oriented resources and their interactions with the domestic economy and energy system are then examined through the lens of the resource curse hypothesis, and the LNG and aluminium industries. These existing resource export frameworks are reviewed for applicability of specific factors to export-oriented green hydrogen production, with applicable factors then compiled into a novel conceptual framework for exporter domestic implications from large-scale exports of green hydrogen. The green hydrogen export superpower (2050) scenario is then quantitatively assessed using the established indicators for energy exporter vulnerability and domestic energy security, comparing it to Australia’s 2019 energy exports profile. This assessment finds that in almost all factors, exporter vulnerability is reduced, and domestic energy security is enhanced by the transition from fossil fuel exports to green hydrogen, with the exception of an increase in exposure of the domestic energy system to international market forces.

In this paper, a probabilistic bi‐objective energy management system (EMS) model is proposed for an energy hub (EH) equipped with renewable energy sources such as photovoltaic and wind turbine connected to the main power grid, boiler, combined heat and power unit, along with thermal and electrical storages, in addition to power to gas (PtG) technology. The proposed EMS model aims to minimize operating costs and maximize the system flexibility index (SFI) while considering demand response programs (DRPs) and uncertainties in both load and generation. To solve the EMS model, a dynamic parameter multi‐objective cuckoo search (DP‐MOCS) algorithm is proposed. The high accuracy and superior performance of the proposed DP‐MOCS algorithm have been verified by solving standard ZDT benchmark functions and comparing the results with those of other multi‐objective optimization algorithms. The performance of the proposed EMS model for the EH is evaluated through simulations conducted in three sections, considering uncertainties and DRPs, as well as neglecting them. Simulation results demonstrate a reduction in costs and an increase in the EH's flexibility following the implementation of a DRP in the EMS. Incorporating DRP into the EMS has resulted in a 6.24% decrease in operating costs and a 3.41% increase in flexibility at the EH. Additionally, considering uncertainties in the EH led to a 2.25% rise in operating costs and a 2.72% decrease in SFI. Moreover, the proposed DP‐MOCS algorithm outperformed other algorithms in addressing the energy management problem.

The idea of using wind power to charge electric vehicles (EVs) has attracted more and more attention nowadays due to the potential in significantly reducing air pollution. However, this problem is challenging on account of the uncertainty in the wind power generation and the charging demand from the EVs. Simulation-based policy improvement (SBPI) has been an important method for decision-making in stochastic dynamic programming and, in particular, for charging decisions of EVs in microgrids. However, the problem of allocating the limited computing budget for the best decision-making in online applications is less discussed. We consider this important problem in this work and make the following three major contributions. First, we show that the significant uncertainty in wind power generation forecasting could make the policy that is the outcome of an SBPI worse than the base policy. Second, we apply two existing methods to address this issue, namely, the optimal computing budget allocation (OCBA) for maximizing the probability of correct selection (OCBA_PCS) and the OCBA for minimizing the expected opportunity cost (OCBA_EOC). The asymptotic optimality is briefly reviewed. Third, we numerically compare the performance of OCBA_PCS and OCBA_EOC with the equal allocation (EA), a principle-based method, and a stochastic scenario-based method on small-scale and large-scale experiments. This work sheds light on the EV charging decision in general. Note to Practitioners-Together with the growing adoption of EVs in modern societies, there goes the challenge of how to satisfy the charging demand. Given the high uncertainty both in the wind power generation and in the charging demand, it is important to make decisions online using up-to-date estimation on the renewable power generation and the charging demand. Simulation-based policy improvement (SBPI) is shown both theoretically and practically to be useful to improve a given base policy in various applications, including this EV charging problem. However, the high uncertainty in forecasting could sometimes make the output of SBPI worse than that of the base policy. In this work, we first use numerical experiments to demonstrate the risk for such scenarios. Then, we propose to use two computing budget allocation procedures to address this issue. The asymptotic optimality of both algorithms is briefly reviewed. We demonstrate their performance on numerical experiments when there are only several EVs and when there are 100 EVs.

Building automation enables the possibility of energy flexibility in buildings. To investigate the motivation and barriers for the energy flexibility in buildings, this study develops a conceptual framework of the readiness for energy flexible buildings by conducting interviews with building automation suppliers, electricity supplier, district heating supplier, distribution system operator, energy service companies, experts in energy and buildings, building managers, and occupants. The two main parts of the framework are building preparation, grid and market conditions following the impacts of regulation and policies, stakeholder collaboration and integrated building automation. A case study of campus buildings is conducted to demonstrate the framework. The result of the case study shows that the main barriers for buildings to provide energy flexibility are 1) many buildings are too old and need to be refurbished, 2) the benefit of providing energy flexibility to the grid is not sufficient, 3) building management systems need to be either installed or upgraded to response to the demand from the grid. Building managers believe that buildings can provide energy flexibility by building automation and distributed energy resources, but they consider energy efficiency to be more important than providing flexibility to the grid. Meanwhile, occupants have different opinions regarding the comfort level of indoor air quality and control, and the differences are based on various factors, e.g. location, room type, and building ages.

An investigation on the impacts of regulatory support schemes on distributed energy resource expansion planning

Energy flexibility investigation of advanced grid-responsive energy control strategies with the static battery and electric vehicles: A case study of a high-rise office building in Hong Kong

Energy flexibility of manufacturing systems helps to meet sustainable manufacturing requirements and is getting significant importance with rising energy prices and noticeable climate changes. Considering the need to proactively enable energy flexibility in modern manufacturing systems, this work presents a component-based design approach that aims to embed energy flexibility in the design of cyber-physical production systems. To this end, energy management using Industry 4.0 technologies (e.g., Internet of Things and Cyber-physical Systems) is compared to the literature on energy flexibility in order to evaluate to what extent the energy flexibility practice takes advantage of Industry 4.0 technologies. Another dimension is the coverage of the life cycle of the manufacturing system which guarantees its sustainable design and the ability to achieve energy flexibility by configuring the energy consumption behaviour. A data-based design approach of the energy-flexible components is proposed in the spirit of the Reference Architectural Model Industrie 4.0 (RAMI 4.0), and then it is exemplified using an electric drive (as a component) in order to show the practical applicability of the approach. The energy consumption behaviour of the component is modelled using machine learning techniques. The digital twin of the studied component is developed using Visual Components virtual engineering environment, then its energy consumption behaviour is included in the model allowing the system integrator/decision-maker to configure the component based on the energy availability/price. Finally, external services in terms of an optimisation module and a deep learning module are connected to the digital twin.

Optimal energy and reserve management of a smart microgrid incorporating parking lot of electric vehicles/renewable sources/responsive-loads considering uncertain parameters

Pre-formed internal insulative panels with impregnated phase change materials (PCM) can significantly increase both the thermal resistance and thermal capacitance of existing or new building envelopes, thereby improving the overall energy performance of buildings. A further advantage is that such measures have the potential to enhance the energy flexibility of the building, thereby offering the possibility of participation in demand side management measures such as demand response programmes. The current literature on building envelope physics lacks research on energy flexibility and demand response, especially in the context of the building envelope integrated design with high latent heat materials such as PCM for demand response applications. The objective of the current study is to examine how the addition of PCM impregnated building envelopes affects both the thermal performance of the building envelope, as well as the wider building energy characteristics when subject to different demand response events. A reference building is utilised, which is a residential detached house with a floor area of 160 m 2 and a south-easterly facing aspect. Another contribution of this study is proposing new energy flexibility indicators taking into consideration envelope pre-cooling and pre-heating periods prior to the demand response event. Simulation results show that shorter envelope pre-cooling periods (0.5 hr) together with longer demand response periods (4 h) are preferable for all envelopes to achieve the maximum power curtailment for cooling. PCM-enhanced envelopes are shown to give best cooling demand shifting and energy flexibility efficiency. The MW PCM-1 and MW PCM-2 envelopes have the highest flexibility efficiency with a value of 244%. For heating, gypsum board enhanced with PCM retrofitted on the envelopes are shown to give an overall good performance in energy flexibility efficiency and in power curtailment compared to the other building envelopes in all durations of an energy flexibility event. For heating, the maximum energy flexibility efficiencies range from 250% for the LW Gypsum Board envelope to 356% for the LW PCM-2 envelope. • Energy flexibility is mapped for building envelopes with sensible and latent TES. • Building envelopes are optimised for different demand response events. • An energy flexibility sensitivity analysis algorithm is developed in EnergyPlus. • In buildings with only sensible TES short demand response events should be used. • Envelopes with PCM have better energy flexibility in long demand response events.

Demand response (DR) programs have become powerful tools of the smart grids, which provide opportunities for the end-use consumers to participate actively in the energy management programs. This paper investigates impact of different DR strategies in a home-energy management system having consumer with regular load, electric vehicle (EV) and battery-energy storage system (BESS) in the home. The EV is considered as a special type of load, which can also work as an electricity generation source by discharging the power in vehicle-to-home mode during high price time. BESS and a small renewable energy source in form of rooftop photovoltaic panels give a significant contribution in the energy management of the system. As the main contribution to the literature, a mixed integer linear programming based model of home energy management system is formulated to minimize the daily cost of electricity consumption under the effect of different DR programs; such as real time price based DR program, incentive based DR program and peak power limiting DR program. Finally, total electricity prices are analysed in the case studies by including different preferences of the household consumer under mentioned DR programs. A total of 26.93 % electricity cost reduction is noticed with respect to base case, without peak limiting DR and 19.93 % electricity cost reduction is noticed with respect to base case, with peak limiting DR.

Electricity spot prices in emerging electricity markets exhibit high volatilities and occasional distinctive price spikes due to the non-storable nature of electricity. Furthermore, the inherent variability and uncertainty of renewable energy generation require balancing supply and demand all the time for electric power systems. In the context of reliable electricity service, demand response (DR) programs allow end-users to adapt their electricity usage to changes in the price of electricity over time. DR programs include price-based and incentive-based DR programs. Real-time pricing (RTP) is a price-based DR program, which charges customers (electric vehicle (EV) users) electricity rates based on the utility’s real-time production costs. In this paper, we propose a financial option that can be used as additional incentives to RTP, which reinforce the encouragement to customers shifting their electric usage aligning the renewable energy supply peak. We value three different price-based DR programs on EVs charging through the average daily electricity cost. Through a realistic study of Hourly Ontario Energy Price (HOEP) in the electricity market, the feasibility of the valuation methodology is demonstrated. Finally, the result of the study shows that the proposed DR program allows electricity utilities to dynamically optimize electric grid operations without increasing the price volatility and provide more reliable service to consumers at a lower price.

Decarbonization plans promote the transition to heat pumps (HPs), creating new opportunities for their energy flexibility in demand response programs, solar photovoltaic integration and optimization of distribution networks. This paper reviews scheduling-based and real-time optimization methods for controlling HPs with a focus on energy flexibility in distribution networks. Scheduling-based methods fall into two categories: rule-based controllers (RBCs), which rely on predefined control rules without explicitly seeking optimal solutions, and optimization models, which are designed to determine the optimal scheduling of operations. Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. The paper also examines studies on the impact of HPs on distribution networks, particularly those leveraging energy flexibility strategies. Key takeaways suggest the need to validate control strategies for extreme cold-weather regions that require backup heaters, as well as develop approaches designed for demand charge schemes that integrate HPs with other controllable loads. From a grid impact assessment perspective, studies have focused primarily on RBCs for providing energy flexibility through HP operation, without addressing more advanced methods such as real-time optimization using MPC or RL-based algorithms. Incorporating these advanced control strategies could help identify key limitations, including the impact of varying user participation levels and the cost-benefit trade-offs associated with their implementation.

Intermittent demand-controlled ventilation for energy flexibility and indoor air quality