• Energy Research

Abstract As an emerging energy storage technology for renewable power utilization, power-to-gas (P2G) requires efficient bidding strategies to increase profit. This paper proposes a bi-level strategic bidding model for P2G with the consideration of a carbon emission trading scheme (CETS), an embedded locational marginal price (LMP) and wind power uncertainty, where the upper level model aims to maximize the profit of P2G facilities and the lower level model carries out the market clearing process. Under a CETS, a P2G facility purchases electricity power according to the LMP and is assumed to sell synthetic natural gas (SNG) and carbon emission permits for revenue. The electricity market clearing process is represented by a stochastic low carbon economic dispatch (ED) model considering the CETS, P2G and wind power uncertainty. On this basis, the formulation of a CETS embedded LMP (CETS-LMP) is presented and analyzed here. The bi-level model is reformulated and converted into mixed-integer linear programming (MILP) using strong duality theory. Case studies performed on a modified PJM 5-bus system and an IEEE 118-bus system verify the effectiveness of the proposed model and demonstrate that considering the CETS can increase the profit of P2G facilities and reduce carbon emission.

In view of modern power system characterized by significant inertia reduction and booming uncertainty, this letter proposes an important operational planning tool to comprehensively analyze frequency stability including both steady-state frequency and rate of change of frequency issues in a probabilistic manner for the first time. The proposed generic framework can tackle various frequency-related uncertainties and accommodate different system frequency response models. The effectiveness and efficiency of the proposed analytical probabilistic assessment are demonstrated by comparing with numerical scenario-based simulation.

With the recent development of advanced metering infrastructure, real-time pricing (RTP) scheme is anticipated to be introduced in future retail electricity market. This paper proposes an algorithm for a home energy management scheduler (HEMS) to reduce the cost of energy consumption using RTP. The proposed algorithm works in three subsequent phases namely real-time monitoring (RTM), stochastic scheduling (STS) and real-time control (RTC). In RTM phase, characteristics of available controllable appliances are monitored in real-time and stored in HEMS. In STS phase, HEMS computes an optimal policy using stochastic dynamic programming (SDP) to select a set of appliances to be controlled with an objective of the total cost of energy consumption in a house. Finally, in RTC phase, HEMS initiates the control of the selected appliances. The proposed HEMS is unique as it intrinsically considers uncertainties in RTP and power consumption pattern of various appliances. In RTM phase, appliances are categorized according to their characteristics to ease the control process, thereby minimizing the number of control commands issued by HEMS. Simulation results validate the proposed method for HEMS.

Industrial and commercial electricity customers have significant potential in providing flexibility for power systems through diverse demand response (DR) programs. However, the industrial and commercial potential of DR is not yet completely understood, especially regarding the emerging and advanced technologies associated with the smart grid. Advances in smart meter technology that allow monitoring and controlling responsive loads in real time will also be key enablers of DR potential. It can be more complex to implement DR for industrial loads if compared to residential loads mainly due to the reliability management that is more vital for industrial plants. Hence, this paper aims at providing a comprehensive review of the most recent advances on industrial and commercial DR. On this basis, this survey first presents the potential and technologies of DR in industrial and commercial sectors. Then, the existing models of DR in the mentioned sectors are presented. The presence of industrial and commercial DR in electricity markets is also investigated. Finally, the main positive and beneficial aspects, as well as challenges and barriers of industrial and commercial DR, are investigated.

The smart grid initiative and electricity market operation drive the development known as demand-side management or controllable load. Home energy management has received increasing interest due to the significant amount of loads in the residential sector. This paper presents a hardware design of smart home energy management system (SHEMS) with the applications of communication, sensing technology, and machine learning algorithm. With the proposed design, consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer. Also, consumers may interact with suppliers or load serving entities (LSEs) to facilitate the load management at the supplier side. Further, SHEMS is designed with sensors to detect human activities and then a machine learning algorithm is applied to intelligently help consumers reduce total payment on electricity without or with little consumer involvement. Finally, simulation and experiment results are presented based on an actual SHEMS prototype to verify the hardware system.

Abstract This paper proposes the concept and model of total supply capability (TSC) curve and total accommodation capability (TAC) curve, which is a novel approach to assess the overall capability of an active distribution network (ADN) based on distribution system security region (DSSR). The TSC curve describes the full load supply capability while the TAC curve depicts the full distributed generation (DG) accommodation capability. For the TSC curve and the TAC curve, an ADN has both the upper curves and the lower curves, because both growth and reduction of either load or DG may result in security criticality, and form the upper and lower security boundaries, respectively. A simple test system and IEEE 33-node test system are used to demonstrate the proposed concepts and models. The TAC curve is compared with existing methods to show its advantages and applications. Additionally, it is found that supply capability and accommodation capability are dual; and the operational conditions of the ADN are divided by the four TSC/TAC curves.

Software design reuse has been discussed in the past at the component level through Design Patterns. A software framework is an approach to achieve software reusability for an entire domain. This paper presents architectural design concepts of a framework for power distribution system analysis. The commonalities of distribution system analysis, including components, topologies, and algorithms are considered. A layered architecture with strict top-down dependency is proposed to decrease software couplings. Interfaces are used to hide the internal structure of each layer. The Composite and Iterator design patterns are used in the framework design. This paper also presents practical examples of developing customized applications that reuse and extend the framework.