Heat storage (HS) instalment in combined heat and power (CHP) plants is a promising solution to increase the adjustability of CHP plants and reduce renewable energy curtailment. Despite the proposed optimisation models and algorithms, it still lacks a dispatching framework for HS facilities to integrate with the existing dispatching system of power grids. This study integrates a coordination control system (CCS) and a plant-level energy management system (PEMS) into the existing dispatching system and proposes the corresponding scheduling method to ensure the rationality, veracity, and timeliness of scheduling. The CCS generates power and thermal generation schedules in multi-time scales, while the PEMS operates as the bridge between the HS facilities and the CCS. Meanwhile, taking into account the actual scheduling requirements, the authors propose a downward spinning reserve capacity (DSRC) preservation method for the dispatch of CHP plants, develop multi-level coordination and progressive refinement strategy for schedules and apply the DSRC as a criterion to obtain the day-ahead, the rolling and the real-time schedules in sequence. Finally, the dispatch simulation results show that the newly proposed CHP dispatching framework can schedule the HS facilities in CHP plants effectively and achieve the desired effect of wind power accommodation.

Notable benefit can be brought by combined operation of coupled electricity-heat system (CEHS) and be enhanced by introducing independent thermal energy storage (ITES). With prevalent constant-flow variable temperature (CF-VT) control strategy of heat network, explicitly formulating CEHS with ITES leads to mixed-integer non-convex programming. Although the problem can be reformulated as a mixed integer second-order cone programming (MISOCP) problem and solved by commercial solvers, improving the computation efficiency still needs more effort. Here, several alternative solution techniques, based on either reformulation or approximation methods, are studied and compared with the original MISOCP formulation. The computation efficiencies as well as on the solution accuracies of various solution techniques or a combination of them are investigated and analysed based on two constructed systems. Simulation results reveal that appropriately selecting formulations of electric and heat networks can effectively improve the performance of solving the original problem.

As the energy crisis becomes more prominent, the issue of energy conservation and recycling is urgent. For large-scale industrial plants, there are various forms of heat energy resources with different grades and energy storages. This study presents the detail modelling of all equipment in the integrated energy system to specify the physical operational constraints. Furthermore, an optimisation model is set up to minimise the total cost of an individual industrial plant, with the consideration of energy production equipment, energy conversion equipment and energy storage equipment in the plant. In particular, the relationship among the heat energy with different grades is described to characterise the cascade utilisation of the heat energy. Finally, a material processing factory with multi-grade heat energy is simulated to verify the effectiveness of the proposed model with energy cascade utilisation.

Converting electricity into hydrogen (power-to-hydrogen – P2H) is proved to be an effective means of energy storage. However, the conversion efficiency of commercial electrolyzers is currently among 60–70% and the storage and transportation cost of hydrogen is relatively high. On this basis, this study promotes the comprehensive usage of P2H as a flexible load in renewable power systems as well as heat sources in the heat network. The model of power-to-heat and hydrogen (P2HH) is established, considering the utilisation of waste heat. This model has further experimentally verified with alkaline electrolyzer groups rated at 46.5 kW. The authors established an optimal control model of a P2HH microgrid and carried out a case study on this model; the results show that temperature plays an important role in the P2HH process because it directly influences the proportion of electricity to generate hydrogen and the proportion of electricity to generate heat. Besides, P2HH helps improve the overall system efficiency despite the possible efficiency reductions of the power to hydrogen process itself.

Owing to the limited operating regions of combined heat and power (CHP) units, the operation of integrated energy systems suffers from low flexibility, low-cost efficiency, renewable curtailment etc. Meanwhile, as the capacity of renewable energies keeps growing and integrating into power systems, various methods, such as installing electric boilers to enable electricity-heat conversion, have been developed to absorb excessive renewables and increase system operation flexibility. To further increase the system operation flexibility, this study explores the possibilities of utilising electrolysers and hydrogen storage tanks to enable electricity-hydrogen-heat conversion. To better visualise the enhanced flexibility, this study presents extra flexibilities from electric boilers, electrolysers and hydrogen tanks as the equivalent operating region expansion for CHP units. In this study, the system is modelled as a mixed-integer optimisation problem which balances the electricity, heat and hydrogen demands in a 24-hour period. A 6-bus test system is used in the case studies to illustrate the effectiveness of implementing electrolysers and hydrogen storage tanks. The optimisation results show the application of hydrogen energy improves the system operation flexibility, reduces wind curtailment, thereby decreasing fuel consumption and carbon emission.

In response to the challenge of improving energy utilising efficiency, multi-carrier energy storage systems, composed of electrical power networks, natural gas networks, heating networks, and other energy networks, are attracting more attention and developing rapidly in recent years. Traditionally, different energy infrastructures are commonly planned and operated independently, which results in less efficient energy usage and resource wasting. Through integrating as multi-carrier energy storage system, different energy infrastructures can be coupled and optimised as one unit. Here, an optimal scheduling for a real multi-carrier energy storage system with hydrogen-based vehicle applications is proposed from an economic point of view. Simulation results suggest the proposed optimal scheduling can help quantify the daily operation cost and achieve considerably operation cost saving by reasonably arranging and utilising all the components in the system.

Advanced adiabatic compressed air energy storage (AA-CAES) has been recognised as a promising approach to boost the integration of renewables in the form of electricity and heat in integrated energy systems. Nevertheless, the uncertainty and variability of renewables spark the consideration of the off-design thermodynamics of AA-CAES, particularly when the heat exchanger is equipped to collect and recycle air-compression heat. Here, the authors propose a general off-design thermodynamic simulation model of AA-CAES incorporating the part-load characteristics of heat exchangers, and validate the effectiveness of the proposed model on a practical AA-CAES demonstration pilot. The results indicate that the simulation model offers an acceptable approximation for the practical operation behaviour of AA-CAES facility and decreases the modelling error from 20.8 to 6.28% compared with the existing model. The proposed simulation model is expected to provide insights into the investigation of the dynamic characteristics of AA-CAES in the future integrated energy system.

With the development of economy and the growth of industrial demands, the peak–valley difference of electric load is ever increasing, calling for the deployment of energy storage units. Advanced-adiabatic compressed air energy storage (AA-CAES) is a promising large-scale energy storage technology and exhibits various advantages in fast response, long service time, low environmental impact and so on. It also has the potential in combined heat-and-power production because heat is a by-product when air is compressed. This study presents a novel AA-CAES-based energy hub and its mathematical formulation considering the pressure behaviours and mass flow rate variations of AA-CAES, and further envisions its business model for the transaction with a power distribution system and a heating system under time-of-use price. A bilevel game-theoretical model is developed to capture the interaction between the two infrastructures through the integrated demand response of the energy hub and investigate the equilibrium state at which none of the stakeholders would like to alter their strategy unilaterally. Results show the interdependence of electricity and heat prices as well as the economic impact of energy hub performance.

Development in utilising the energy storage system (ESS) has led to increasing flexibility in the planning of energy networks. This study presents optimal day-ahead scheduling for multi-carrier energy networks in the presence of ESS. To achieve this purpose, a new economic approach for ESS is proposed that aims to utilise for generation management in the multi-carrier networks. Also, the proposed economic approach presents a novel pricing policy that reduces the total cost of the system at each time interval. Therefore, the proposed pricing policy results in obtaining the charging and discharging pattern of ESS adaptively. To solve the multi-period optimal energy flow problem in multi-carrier energy networks, this study utilises the well-known teaching-learning based optimisation algorithm. The investigated multi-carrier energy system consists of electrical, natural gas and district heating sub-networks in which an ESS is included in the electrical sub-network. The performance of the proposed approach is validated by comparing the ESS daily charging and discharging pattern and the daily load demand curve. The results show that the proposed method could be utilised for load shedding and tracking the load demand curve more effectively.

This study proposes a stochastic optimisation programming for scheduling a microgrid (MG) considering multiple energy devices and the uncertain nature of renewable energy resources and parking lot-based electric vehicles (EVs). Both thermal and electrical features of the multi-energy system are modelled by considering combined heat and power generation, thermal energy storage, and auxiliary boilers. Also, price-based and incentive-based demand response (DR) programs are modelled in the proposed multi-energy MG to manage a commercial complex including hospital, supermarket, strip mall, hotel and offices. Moreover, a linearised AC power flow is utilised to model the distribution system, including EVs. The feasibility of the proposed model is studied on a system based on real data of a commercial complex, and the integration of DR and EVs with multiple energy devices in an MG is investigated. The numerical studies show the high impact of EVs on the operation of the multi-energy MGs.

Multi-carrier energy systems (MCESs) can be formed by the integration of various energy infrastructures including power and natural gas systems. The proliferation of bidirectional energy conversion units in an MCES can set the stage for a more resilient and robust system. This study shows how bidirectional energy conversion units and storage devices can be optimally scheduled within an MCES for provision of various regulation services to the grid operator. To that end, a new model is proposed for optimal scheduling of power-to-gas (PtG), gas-fired generation, and gas storage units in an MCES. The model aims to facilitate integration of renewables, utilise gas, and power price arbitrage, provide regulation services to the real-time (RT) market, and contribute to the system restoration. New indices that quantify the contribution of the MCES operator to RT and ancillary service markets are proposed. The proposed model is validated technically and economically by using a test system historical operating data. Numerical results demonstrate that while the proposed model is technically feasible, it also enhances the economic viability of the grid operator.

The substantial presence of different uncertainties in energy systems highlights the need for probabilistic analysis of operational and planning studies. Motivated by this fact, a new stochastic planning framework for energy hubs (EHs) is presented in this study based on the probability transformation concept. In the proposed framework, a measure of relative-likelihood impact is developed to estimate the importance of uncertainty sources in the studies, which, in turn, can remarkably reduce the overall complexity of stochastic problems. Step-by-step algorithm for implementation of the proposed framework on planning studies of an EH is also addressed in this study. Three different case studies are introduced, and the results provide some insightful information regarding the impact of different uncertainty sources in the system.

Vehicle-to-grid (V2G) technology plays an important role in solving the large-scale disordered charging and discharging behaviour of the electric vehicles (EVs). V2G mode based on microgrid is used for multi-objective optimisation coordination between the EVs and the power grid, and a multi-objective optimisation model, in which minimum grid load fluctuation, maximum renewable energy utilisation and maximum benefits for the EV users as the optimisation objectives are established. In order to solve the optimisation model, searching valley scheduling algorithm, variable threshold optimisation algorithm and variable charge/discharge rate optimisation algorithm are proposed successively. In order to verify the control effect, the three proposed algorithms are compared with the without optimisation algorithm. The results show that the three proposed algorithms, in a manner, could improve the imbalance between power supply and demand of the microgrid; increase utilisation of renewable energy; and bring certain benefits to the EV users. By analysis of experimental data, the control effect of variable charge/discharge rate scheduling algorithm is the best in the three scheduling algorithms.

This study proposes a novel control strategy for a hybrid energy storage system (HESS), as a part of the grid-independent hybrid renewable energy system (HRES) which comprises diverse renewable energy resources and HESS – combination of battery energy storage system (BESS) and supercapacitor energy storage system (SCESS). The proposed control strategy is implemented into two parts: in the first part, HESS controller is designed and implemented to maintain the active power balance among different constituents of HRES and regulate DC-link voltage (V _DC) under surplus power mode and deficit power mode. The low-frequency components of imbalance power are diverted to BESS while the high-frequency components are diverted to SCESS while maintaining the state of charge (SoC) constraints of HESS thereby reducing stress on BESS. In the second part, inverter controller is designed and implemented to maintain AC voltage and frequency within limits in the events of perturbation. The proposed HESS along with its novel control strategy, as implemented in the HRES, is a new proposition in this study. The detailed model of HRES is simulated in MATLAB/Simulink and the results prove the efficacy of the control strategy. Further, hardware-in-loop real-time simulation studies using OPAL-RT real-time simulator demonstrate the feasibility of hardware implementation of HRES with the proposed control strategy.

For the energy router (ER) switching on/off frequently to select the optimal route, this study mainly solves the problem of on/off-grid switching and islanding detection of grid-connected inverters on the AC side of ERs. A control strategy based on disturbance observer is proposed to suppress the disturbance of the output current to the output voltage. At the same time, the improved pre-synchronisation method is used to achieve fast synchronisation of voltage amplitude and phase. Then, an improved frequency-shift method is proposed for islanding and non-detection zone detection for droop control grid-connected inverters. Finally, the proposed methods are verified by simulation and experiments.

The many well-established advantages of distributed generation (DG) make their usage in active distribution networks prevalent. However, uncontrolled operation of DG units can negatively interfere with the performance of other equipment, such as tap-changers, in addition to resulting in sub-optimal usage of their potential. Thus, adequate scheduling/control of DG units is critical for operators of the distribution system to avoid those adverse effects. A linearised model of a multi-objective method for coordinating the operation of photovoltaics, battery storage systems, and tap-changers is proposed. Three objective functions are defined for simultaneously enhancing voltage profile, minimising power losses, and reducing peak load power. The formulated multi-objective problem is solved by means of the epsilon-constraint technique. A novel decision-making methodology is offered to find the Pareto optimality and select the preferred solution. To assess to proposed model's performance, it is tested using 33-bus IEEE test system. Consequently, tap-changers suffer lessened stress, the batteries state-of-charge is kept within adequate limits, and the DG units operation is at higher efficiency. The obtained results verify the effectiveness of this approach.