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Optimizing the scheduling of heating, ventilation, and air-conditioning (HVAC) systems in multizone buildings is a challenging task, as occupants in various zones have different thermal preferences dependent on time-varying indoor and outdoor environmental conditions and price signals. Price-based demand response (PBDR) is a powerful technique that can be used to handle the aggregated peak demand, energy consumption, and cost by controlling HVAC thermostat settings based on time-varying price signals. This paper proposes an intelligent and new PBDR control strategy for multizone office buildings fed from renewable energy sources (RESs) and/or utility grid to optimize the HVAC operation considering the varying thermal preferences of occupants in various zones as a response of real-time pricing (RTP) signals. A detailed mathematical model of a commercial building is presented to evaluate the thermal response of a multizone office building to the operation of an HVAC system. The developed thermal model considers all architectural and geographical effects to provide an accurate calculation of the HVAC load demand for analyses. Further, Occupants' varying thermal preferences represented as a coefficient of a bidding price (chosen by the occupants) in response to price signals are modeled using an artificial neural network (ANN) and integrated into the optimal HVAC scheduling. Furthermore, a control mechanism is developed to determine the varying HVAC thermostat settings in various zones based on the ANN prediction model results. The effect of the proposed strategy on aggregator utility with wider implementation of the developed mechanism is also considered. The optimization problem for the proposed PBDR control strategy is formulated using a building's thermal model and an occupant's thermal preferences model, and simulation results are obtained using MATLAB/Simulink tool. The results indicate that the proposed strategy with realistic parameter settings shows a reduction in peak demand varying from 7.19% to 26.8%, contingent on the occupant's comfort preferences in the coefficient of the bidding price compared to conventional control. This shows that the proposed approach successfully optimizes the HVAC operation in a multizone office building while maintaining the preferred thermal conditions in various zones. Moreover, this technique can help in balancing the energy supply and demand due to the stochastic nature of RESs by cutting electricity consumption. • A multizone office building with varying thermal comfort preferences is controlled. • Occupants preferences in coefficient of bidding price are model using neural network. • An algorithm is developed to control the HVAC thermostat setting in various zones. • An intelligent price-based demand response control strategy is proposed. • Optimal HVAC scheduling model is presented for peak load and cost savings. [ABSTRACT FROM AUTHOR]

• A residential energy management model is presented for two-stage energy trading. • Stochastic bi-level optimization minimizes cost and inconveniences under uncertainty. • Building thermal model presented for thermal comfort reservation for the user. • Battery degradations are incorporated for actual cost minimization. • Better cost savings than state-of-the-art methods providing up to 51% reductions. Bi-directional electricity trading of demand response (DR) and transactive energy (TE) frameworks allows the traditionally passive end-users of electricity to play an active role in the local power balance of the grid. Appropriate building energy management systems (BEMSs), coupled with an optimized bidding strategy, can provide significant cost savings for prosumers (consumers with on-site power generation and/or storage facility) when they participate in such bi-directional trading. This paper presents a BEMS with an optimization-based scheduling and bidding strategy for small-scale residential prosumers to determine optimal day-ahead energy-quantity bids considering the expected cost of real-time imbalance trading under uncertainty. The proposed scheduling and bidding strategy is formulated as a stochastic bi-level minimization problem that determines the day-ahead energy-quantity bids by minimizing the energy cost in the upper level considering expected cost of uncertainty, whereas a number of lower-level sub-problems ensure optimal operation of building loads and distributed energy resources (DERs) for comfort reservation, minimization of consumers' inconveniences and degradation of residential storage units. A modified decomposition method is used to reformulate the nonlinear bi-level problem as a mixed-integer linear programming (MILP) problem and solved using 'of the shelf' commercial software. The effectiveness of the proposed BEMS model is verified via case studies for a residential prosumer in Sydney, Australia with real measurement data for building energy demand. The efficacy of the proposed method for overall financial savings is also validated by comparing its performance with state-of-the-art day-ahead scheduling strategies. Case studies indicate that the proposed method can provide up to 51% and 22% cost savings compared to inflexible non-optimal scheduling strategies and deterministic optimization-based methods respectively. Results also indicate that the proposed method offers better economic performance than standard cost minimization models and multi-objective methods for simultaneous minimization of energy cost and user inconveniences. [ABSTRACT FROM AUTHOR]

In this research, a computationally-inexpensive stochastic MILP model is proposed for the optimal placement of hydrogen fuel stations (HFSs) in power systems with high penetration of renewables, while power system operator sells its extra hydrogen in a day-ahead local hydrogen market. In the developed model, a linearisation strategy is proposed to transform the nonlinear binary terms into linear terms and the uncertainties of electricity and hydrogen demands, photovoltaic (PV) generation and hydrogen market prices are modeled as scenarios. Any available HFS includes an electrolyzer and a hydrogen storage system. As the penetration of renewables in the studied power system is high, most of the produced hydrogen is green. According to the results, system operator uses the potential of hydrogen storage systems and responsive electricity demands to sell more hydrogen to the local hydrogen market and increase its profit. In times with higher hydrogen prices, system operator commands both shift-down in electricity demands and discharge mode for batteries to be able to sell more electricity and make more profit; on the other hand, in times with lower hydrogen prices, system operator commands shift-up in electricity demands and charge mode for batteries. The results show that demand response program increases expected profit of system by 4.7%. The results confirm that addition of HFSs strongly decreases PV curtailment. The impact of the participation in hydrogen market on system profit is assessed. The sensitivity of HFS profit to the number of HFSs, size of electrolyzers and demand response participation factor is assessed. • Optimal location of hydrogen fuel stations (HFSs) is found in power systems. • A linearisation strategy is used to transform bi-linear terms into linear terms. • Uncertainties of demands, PV generation and hydrogen market price are considered. • The sensitivity of the power system profit to model parameters is assessed. • Impact of the participation in hydrogen market on power system profit is assessed. [ABSTRACT FROM AUTHOR]

Transactive Energy (TE) is envisaged as an advanced demand response (DR) variant to leverage the flexibility of distributed energy resources (DERs) for enhancing energy balance and network management in modern power systems. However, there have been limited implementations of TE frameworks for low voltage (LV) residential networks to capture the underutilised flexibility potential of DER-equipped residential prosumers. The main purpose of this paper is to identify the rationale behind this gap in light of recent advances in TE-based energy management for residential networks. As such, first, we identify the motivation and significance of the evolution of TE framework from traditional DR schemes by reviewing their relative efficacies in utilising demand-side flexibility of DER-rich residential networks for enhancing energy balance and local network management. Second, we provide an overview of the key components of the TE framework that are essential to facilitate active negotiation and trading of demand-side flexibility in residential networks. Third, we review the state-of-the-art TE methodologies and industry projects that have utilised demand-side flexibility of residential prosumers. Finally, several challenges relevant to TE frameworks in LV residential networks are identified followed by some concluding remarks at the end of the paper. • Demand flexibility potential is analysed for residential prosumers. • The evolution of transactive energy from demand response is rationalised. • A comprehensive overview of the transactive energy management framework is presented. • State-of-the-art grid-supporting transactive energy models are surveyed. • Key challenges of transactive energy for residential applications are identified. [ABSTRACT FROM AUTHOR]