Building energy consumption has taken up to 40% of global energy use, which substantially increases the resources for greenhouse gas emissions. Among various sectors, the commercial building sector is inevitably more favorable as the pioneer for decarbonization under the current power system structure due to the high peak electricity demand and inefficient energy management policy. This study investigates an efficient strategy for coordinating energy usage for commercial buildings to enhance the power system decarbonization and energy efficiency. First, this article introduces an Office Energy Management system for a grid-connected office building, incorporating electric vehicles, batteries, a lighting system, and a heating, ventilation, and air conditioning system. Then, an adjusted carbon emission flow model for dynamically tracking the carbon footprint is presented to fit the commercial building scenario. Finally, a novel human–machine learning (HML) system, combining human intelligence and reinforcement learning methods, is formulated to optimize energy management strategies. Unlike the traditional artificial intelligence technique, the HML paradigm enables humans and machines to collaborate at the energy management level, significantly improving algorithm performance. The proposed hybrid approach, using the HML method, offers up to 8.54% higher net benefits than the other state-of-the-art reinforcement approach.

This article presents, for the first time, the steady-state analysis of composite ac–dc grids embedded with solid-state transformers (SSTs) taking into account the high-frequency transformers in the dc–dc converters of the SSTs. A novel steady-state model of the SST is presented in this article, which includes loss calculations, and facilitates steady-state and impact analysis without complex and time-consuming time-domain simulation. The new steady-state model of the SST proposed in this article was validated using the time domain simulation of the detailed model in a two-bus topology on the MATLAB/Simulink platform. Subsequently, the proposed approach has been implemented in two additional case studies to validate its enhanced performance when utilizing SSTs and demonstrate how the proposed model can be employed for steady-state analysis of the modern grids that encompass a diverse range of converter-interfaced renewable energy generation sources, as well as the system that contains both ac and dc loads.

Rooftop photovoltaic (PV) systems and plug-in electric vehicles (PEVs) will be increasingly utilized by residential customers due to their economic and environmental benefits. However, the intermittent power produced by PVs and the additional loads required for the PEV charging can cause power quality disturbances in the low-voltage distribution networks (LVDN). An adaptable correlated control (ACC) is proposed for the decoupled optimization of the active and reactive power, where energy cost can be minimized through the local demand-side management (DSM) of residential loads. Further, the optimal allocation of the sparse capacity of inverters for reactive power compensation to maintain the voltage variations and the mitigation of voltage unbalances within the allowable limits is proposed. The proposed ACC provides a supervisory control to observe and modify the voltage quality of the LVDN without the need for real-time collaboration between controllers. The impacts of the integration of installed rooftop PVs and PEV charging for unbalanced three-phase LVDNs are assessed rigorously using a versatile simulation platform. Also, the suitability of the proposed control strategy for DSM and the alleviation of voltage fluctuations and unbalances are assessed.

The energy management of the fast-charging stations (FCSs) in power systems, while maintaining the power quality and the grid codes, can be particularly challenging, given the highly dynamic load curve, the heterogeneous PEV charging, and the inconvenience to the FCS customers. In this article, a consecutive horizon-based energy management (CHEM) process is proposed for the optimal techno-economic operation of the FCSs integrated with battery energy storage systems (ESS). The proposed CHEM process employs a central supervisory control and a resilient distributed control, for the energy management of FCSs and the active and reactive power control to maintain the grid codes. As such, the grid operator can realize and direct the economic and technical operation of the FCSs with low computational burden, without the need for real-time collaboration with the FCSs. Moreover, the FCSs can autonomously cope with the volatility of PEV charging with the use of the ESS. Furthermore, a novel Volt/VAr control is proposed to confine the unpredicted voltage deviations, including voltage sags, within an acceptable limit. The heterogeneous PEV charging is modeled using the queuing theory, while the inherent characteristics of the lithium-ion batteries are considered. Through multiple case studies, it is shown that the developed CHEM process can provide lower costs and a leveled active power profile for the FCSs, while ensuring that the voltage deviations are well confined and the convenience of PEV owners can still be met.

Self-organisation provides a suitable paradigm for developing self-managed complex distributed systems, such as grid computing and sensor networks. In this paper, an integrative self-organisation mechanism is proposed. Unlike current related studies, which propose only a single principle of self-organisation, this mechanism synthesises the three principles of self-organisation: cloning/spawning, resource exchange and relation adaptation. Based on this mechanism, an agent can autonomously generate new agents when it is overloaded, exchange resources with other agents if necessary, and modify relations with other agents to achieve a better agent network structure. In this way, agents can adapt to dynamic environments. The proposed mechanism is evaluated through a comparison with three other approaches, each of which represents state-of-the-art research in each of the three self-organisation principles. Experimental results demonstrate that the proposed mechanism outperforms the three approaches in terms of the profit of individual agents and the entire agent network, the load-balancing among agents, and the time consumption to finish a simulation run.

In some real systems, e.g., distributed sensor networks, individual agents often need to form coalitions to accomplish complex tasks. Due to communication and computation constraints, it is infeasible for agents to directly interact with all other agents to form coalitions. Most previous coalition formation studies, however, overlooked this aspect. Those studies did not provide an explicitly modeled agent network or assumed that agents were in a fully connected network, where an agent can directly communicate with all other agents. Thus, to alleviate this problem, it is necessary to provide a neighborhood network structure, within which agents can directly interact only with their neighbors. Toward this end, in this paper, a self-adaptation-based dynamic coalition formation mechanism is proposed. The proposed mechanism operates in a neighborhood agent network. Based on self-adaptation principles, this mechanism enables agents to dynamically adjust their degrees of involvement in multiple coalitions and to join new coalitions at any time. The self-adaptation process, i.e., agents adjusting their degrees of involvement in multiple coalitions, is realized by exploiting a negotiation protocol. The proposed mechanism is evaluated through a comparison with a centralized mechanism (CM) and three other coalition formation mechanisms. Experimental results demonstrate the good performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a brief survey of current coalition formation research is also provided. From this survey, readers can have a general understanding of the focuses and progress of current research. This survey provides a classification of the primary emphasis of each related work in coalition formation, so readers can conveniently find the most related studies.