With the development of smart grid technologies, some of the electric demands which are traditionally considered fixed and inflexible will become promising distributed energy resources in future power systems. However, the participation of small scale or household energy sources into balancing power might challenge the operation of electric distribution systems and cause congestions. This paper presents a distribution congestion price (DCP) based market mechanism to alleviate possible distribution system congestions. By employing the locational marginal pricing model, the proposed DCPs are able to reflect the real congestion cost and further direct the schedule of the responses of electric demands. Based on the NordPool Spot market structure, the interactions between aggregators and the distribution system operator are discussed, and the procedure for calculating DCPs is proposed. Finally, a practical Danish 60kV/10.5kV distribution system is employed as the test case to verify the proposed method for mitigating congestion.

A scenario-based stochastic decision-making model is proposed in this paper to determine the optimal strategy for the operation of integrated natural gas generating unit (NGG) and power-to-gas conversion (P2G) facilities in energy and regulation markets. Using the proposed strategy, the coordination of NGG and P2G facilities will provide a higher market payoff than that of independent NGG and P2G participation. The market price uncertainty is simulated in multiple scenarios using the Latin hypercube sampling method and the conditional value-at-risk strategy is adopted for evaluating the financial risks introduced by price uncertainties. The optimal bidding strategy is developed for both P2G and NGG operations and the Shapley-value method is employed to allocate the market payoff among NGG and P2G facilities. A case study which is based on the Pennsylvania, New Jersey, and Maryland market data is employed to verify the effectiveness of the proposed model and examine the characteristics of the proposed bidding strategy for the optimal operation of integrated NGG and P2G facilities.

The purpose of distribution network reconfiguration (DNR) is to determine the optimal topology of an electricity distribution network, which is an efficient measure to reduce network power losses. Electricity load demand and photovoltaic (PV) output are uncertain and vary with time of day, and will affect the optimal network topology. Single-hour deterministic DNR is incapable of handling this uncertainty and variability. Therefore, this paper proposes to solve a multi-hour stochastic DNR (SDNR). Existing solution methods for DNR are either inaccurate or excessively time-consuming, and are therefore incapable of solving multi-hour SDNRs for large distribution networks. In this regard, a switch opening and exchange (SOE) method is proposed. Starting from a looped network with all switches closed, the SOE consists of three steps. The first step is to sequentially open the switches until all of the loops are opened. The second and third steps modify the status of branches obtained in the first step to obtain better radial topologies. Five test systems are used to validate the accuracy and fast solution speed of the SOE and the superiority of multi-hour SDNR over single-hour deterministic DNR.

The intermittency introduced by the increasing integration of distributed renewable energy sources is challenging the efficient operation of residential distribution systems. A promising solution to tackle this challenge is the implementation of residential demand response through responsive household appliances such as heat pumps, refrigeration devices, and energy storage units. In this paper, a peer-to-peer energy trading platform among residential houses is proposed to coordinate demand response schemes and level off potential generation/consumption disturbances in the hour-ahead intraday context. First, the day-ahead and intraday energy management models for residential houses are established considering the characteristics of responsive household appliances and energy storages. The discomfort and possible economic losses for performing demand responses are quantified with respect to the risk preferences of residential customers. The peer-to-peer energy trading platform is developed and a double auction mechanism employed to promote the collaborative demand response schemes in the face of disturbances. An optimal bidding strategy of residential houses is also proposed. The feasibility of the proposed models and bidding strategy are verified through case studies. It is also illustrated that the residential demand response schemes and intraday peer-to-peer energy trading are effective in managing the uncertainties of load demand and renewable generation.

Power systems have been transformed into cyber-physical systems that integrate electric grids with advanced information technology and operational technology. To ensure reliable and resilient operations of such systems, it is important to understand the system vulnerability and quantify system resilience. This article provides an overview of existing work on vulnerability assessment and resilience quantification related to cyber-physical power systems, and it identifies research gaps and opportunities to enhance resilience. Specifically, we first review the definition and architecture of cyber-physical power systems, and then, summarize existing approaches to assess vulnerability and resilience. Later, we identify several research gaps that have not been well addressed yet and point out possible future work to fill the gap. Although this article focuses on cyber-physical power systems, the research can also benefit stakeholders of other critical infrastructures.

Dynamic thermal rating (DTR) is an important smart grid technology that can bring considerable economic benefits. One of the most important benefits of DTR is to postpone new investment. This paper proposes a novel stochastic transmission expansion planning (STEP) model considering the DTR of overhead lines. The objective function of the STEP model includes operational costs and the investment costs of new line construction and DTR systems installation. The model can determine where to build new lines and install DTR systems. The model can not only realize the benefits that occur when the DTR is higher than the static thermal rating (STR) but also avoid overload risk, i.e., the power flow on a line being larger than the line's real capacity, caused by the DTR being lower than the STR. The model can consider both the voltage magnitude and phase angle of each bus. The model is linearized and therefore can be effectively solved by a Benders decomposition method. Furthermore, a new way of scenario reduction is proposed to obtain a better set of reduced scenarios. The effectiveness of the model is verified on a modified IEEE reliability test system and a modified IEEE 300-bus system.