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The article presents the discussion on deployment of distributed energy resources (DERs). Topics include technical issues with protection placement and coordination, voltage and power violation, power quality, and energy losses; and new business model in power systems motivating electricity end-users for playing an active role and participating in electricity transactions and reducing carbon emissions.

Bidirectional power flow is becoming increasingly commonplace in modern distribution systems with the integration Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs). This has facilitated in bringing generation closer to end‐consumers, thus leading to the emergence and rapid growth of microgrids. Additionally, due to the technological revolution in Information and Communications Technology (ICT), innovative business models have developed where willing and able participants of the power system can participate in the electricity market. In line with this evolution of both the electric power system and its participants, Energy‐as‐a‐Service (EaaS) is emerging as an increasingly prominent business model to address electrical grid challenges economically. EaaS encourages electricity grid customers to play a more active role and participate in different electricity markets. This paper introduces and simulates a variant of a Transactive Energy (TE) trading algorithm for microgrids. The paper describes the model in brief how multiple microgrids assets can trade electricity to ensure more efficient and economical local resource utilization. The main motivations of this research paper is to validate and test the suitability of the TE trading algorithm for a variety of conditions. The coordinated TE trading model can optimize expansion and investment planning especially at the distribution level. [ABSTRACT FROM AUTHOR]

The current electricity market trading mechanism mainly includes the bilateral negotiation transaction and centralized transaction, with defects such as information asymmetry, insufficient competition, and weak price discovery. Thus, a new double‐market parallel trading mechanism is proposed to improve the efficiency of the competitive electricity market in this paper. Regard the electricity market as a price‐searching market and divide into two sub‐markets composed of a price‐searching market for buyers and a price‐searching market for sellers. Market entities can trade synchronously in two sub‐markets as price searchers or bidders. Searchers are responsible for organizing transactions in line with the market clearing rule library, and bidders participate in transactions initiated by searchers. Under the parallel mechanism, the traditional trading mechanism is also applicable. the framework of the proposed mechanism is presented from four aspects, including the description, clearing process, operation mode, and linkage between two sub‐markets. Then, a market clearing model of the proposed mechanism is built with several traditional transaction modes as special cases of the presented double‐market parallel transactions. Finally, taking the centralized transaction as an example, results show that the proposed mechanism can effectively provide market entities more chances to win contracts, and enhance the market liquidity. [ABSTRACT FROM AUTHOR]

Due to the development of new technologies, change of generation mix and appearance of newly formed energy supply hubs, there is a large year‐on‐year change in the marginal loss factors in power systems. Since any change of marginal loss factors could have significant impacts on payment of loads and profitability of generators, it is necessary to carry out a comparative study on the loss factor‐based locational marginal pricing methods. Considering that a systematic comparison of various locational marginal pricing methods has not been reported in existing publications, this work presents a comparative study of the loss factor‐based locational marginal pricing methods that are widely adopted in electricity markets. Advantages and disadvantages of each locational marginal pricing method are explored in detail, and could serve as references in selecting appropriate locational marginal pricing methods in practice. The selected five locational marginal pricing models are tested in two standard power systems, that is, the IEEE 5‐bus and 39‐bus systems. Then, through numerical experiments and detailed analysis, key findings about the reference point dependency of loss factors, accuracy of loss estimation, load payment, generation income, and market settlement surplus are summarised and elaborated. It is found that marginal loss factors‐based locational marginal pricing methods tend to produce a higher market settlement surplus and can lead to a lower generation income than other locational marginal pricing methods. [ABSTRACT FROM AUTHOR]

Resiliently designed and constructed integrated gas‐electric distribution networks (GEDNs) against natural disasters are crucial to social welfare. In this study, a two‐stage robust optimisation‐based co‐expansion planning model is proposed to attain an integrated GEDN with a given resilience level, by optimising the investment strategies of hardening and selective expansion of power distribution feeders and natural gas pipelines, as well as the location and capacity of natural‐gas‐fired distributed generation. In the first stage, the overall annual investment and operation cost is minimised under normal operation conditions while in the second stage, the feasibility of the investment decisions under the identified worst‐case natural disaster scenario is checked with an adjustable load shedding cost criterion. The proposed model is formulated as a mixed integer second‐order cone programming problem with the column and constraint generation algorithm employed to seek the optimal solution. Case studies on two integrated GEDNs demonstrate the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a Stackelberg game‐based optimal energy management model for a microgrid with commercial buildings (CBs), which include a cluster of flexible loads, such as a heating, ventilation, and air conditioning (HVAC) system and a lighting system. In particular, the microgrid operator (MGO) determines the optimal energy management scheme while the CBs enjoy a dynamic pricing tariff to adjust their consumption patterns for cost saving. The interactions between MGO and CBs are formulated as a bi‐level optimisation problem where the MGO behaves as a leader and CBs act as followers. The proposed model is transformed into a mixed integer linear programming (MILP) problem by jointly using the Karush–Kuhn–Tucker (KKT) condition and the strong duality theory. Besides, the effects of correlated solar irradiance and solar illuminance uncertainties on the load profile are taken into account through invoking the Nataf transformation‐based 2M + 1 point estimate method (PEM). Finally, case studies are served for demonstrating the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Identification of coherent generators (CGs) in power systems is one of the key steps in determining controlled islanding strategies. In this study, a wide‐area measurement system (WAMS) and agglomerative hierarchical clustering (AHC) algorithm based coherency identification method is presented for interconnected power systems with aggregated renewable sources. First, the trajectories measured by WAMS are transformed to the centre of inertia based ones for better representing the dynamic behaviour of a given power system, and ten trajectory dissimilarity indexes are presented for determining the similarity of the trajectories of any two generators. Second, a CRITIC (CRiteria Importance Through Intercriteria Correlation) based method, in which entropy and the Spearman's rank correlation coefficient are integrated for reflecting the differences and correlations among multiple indexes, respectively, is presented to integrate the trajectory dissimilarity indexes. Next, the AHC algorithm is utilised to identify CGs. Finally, a modified New England–New York interconnected power system with a large number of renewables, a simplified actual Western Interconnection power system in North America and the eastern part of Guangdong power system in China with a recorded oscillation event happened are utilised to demonstrate the proposed wide‐area coherency identification methodology. [ABSTRACT FROM AUTHOR]

For cross‐regional transmission projects, the two‐part transmission pricing mechanism is suggested so as to promote the sustainable development of cross‐regional electricity trading. In the two‐part transmission pricing mechanism, appropriately determining the capacity charging ratio (CCR) is an important issue not well solved. Given this background, a Z‐number‐based risk‐minimised negotiation model is developed for a transmission company and a power purchaser to achieve an agreeable CCR under incomplete information. The uncertainty distribution of the future annual electricity transmission quantity is first estimated by the Z‐number‐based multiple Z‐valuations; and then, the benefit and risk loss measured by the well‐established conditional value at risk (CVaR) are analysed for the participating two parties. Subsequently, the negotiation model where each negotiator is to minimise its risk loss under a given lowest acceptable benefit constraint and the estimations of the opponent's risk tolerance and negotiation strategy is presented to determine the optimal offer. Finally, the ± 500 kV Xiluodu−Guangdong direct current (DC) transmission project in the southern region of China is employed to demonstrate the basic characteristics of the proposed model. [ABSTRACT FROM AUTHOR]

Efficient and well‐timed investment in distributed generation (DG) in distribution networks in the electricity market environment presents a big challenge for distribution companies. In this study, a real option valuation framework is proposed to determine the optimal investment strategies for DG including the investment location, size, and timing. Within the proposed framework, the profit from investing in DG is modelled, where the benefits include the operation cost savings and capacity update deferral benefit compared with a no‐DG‐investment scenario over the study period. Future power demands and electricity prices are modelled as stochastic variables. The candidate DG investment plans are considered as multiple mutually exclusive options, and the corresponding managerial flexibility for seizing opportunities and mitigating risk of loss upon an unfavourable unfolding of future uncertainties is assessed with real option analysis using the extended least square Monte Carlo method. The distribution of future investment strategies and optimal initial investment threshold levels under various scenarios are analysed in a case study to demonstrate the characteristics of the proposed framework and methodology. [ABSTRACT FROM AUTHOR]

Parallel restoration is an efficient way to speed up the restoration process after a wide‐area outage or a complete blackout of a power system. To implement efficient parallel restoration, an appropriate network partitioning method is necessary. Given this background, a two‐step network partitioning strategy for parallel power system restoration is presented. In order to minimise the restoration time of generating units, a grouping model for non‐black‐start units is developed first. Based on the graph theory, a power network partitioning model is presented with the objectives of minimising the number of the interconnected lines and maximising the electrical distance of the interconnected lines among different restoration subsystems. The well‐established AMPL/CPLEX is employed to solve the developed optimisation models. Finally, the IEEE New England 10‐unit 39‐bus system and a part of Zhejiang provincial power system in China are employed to illustrate the feasibility and effectiveness of the developed models. [ABSTRACT FROM AUTHOR]

The applications of battery energy storage systems (BESSs) in power systems have been paid increasing attention in recent years. Apart from all the benefits that BESSs can bring to the power systems, their potential applications in assisting power system restoration after a major blackout have not yet been systematically investigated. The flexible charging and discharging characteristics of BESSs could help maintain the balance between power supply and demand during the system restoration process, thus the recovery speed of the power system concerned after a blackout could be accelerated. Given this background, the applications of BESSs in power system restoration is investigated. First, the potential applications of BESSs during power system restoration process are discussed. A multi‐objective optimisation model is next proposed, aiming at minimising the number of circuit breaker operations and outage durations of both the non‐black‐start generating units and the important loads. Meanwhile, the application of intermittent renewable energy sources for improving power system restoration is also discussed. The operation strategy of BESSs will also be optimised after the system restoration scheme is attained through the multi‐objective optimisation. The proposed method is demonstrated by numerical tests on the IEEE 30‐bus and 162‐bus test systems. [ABSTRACT FROM AUTHOR]

The energy stored in the batteries of electric vehicles (EVs) could be employed for starting generators when a blackout or a local outage occurs. Considering the feature of the battery swapping mode, an available capacity model of the batteries in a centralised charging station is first developed. Then, the authors analyse the start‐up characteristics of a generator powered by batteries and propose a bi‐level optimisation‐based network reconfiguration model to determine the restoration paths with an objective of maximising the overall generation capability. In the upper‐level optimisation model, the generator start‐up sequence is optimised, whereas the restoration paths are optimised in the lower‐level one. Moreover, they consider the uncertainties associated with the available capacity of the batteries. The bi‐level optimisation model for the network reconfiguration is developed in the chance‐constrained programming framework and solved by the well‐established particle swarm optimisation algorithm. Finally, case studies are employed to demonstrate the effectiveness of the presented model. Simulation results show that a centralised EV charging station could act as a power source to effectively restore a power system without black‐start (BS) generators or with insufficient cranking power from BS generators, and the presented model could be used to guide actual system restorations. [ABSTRACT FROM AUTHOR]

With the ever‐increasing penetration level of renewable energy generation in a power system, more uncertainties are introduced and hence risk management in the electricity market associated is becoming a more difficult issue for a market participant in the context of optimising his/her portfolio. Among a lot of risk factors in the competitive electricity market environment, the highly volatile electricity price contributes most to the financial risk of the power portfolio, especially in a short‐term risk management scenario such as the spot market and real‐time balancing market. Some research work has shown that the fluctuations of electricity prices exhibit multifractal characteristics, but less work has been done on the price volatility risk evaluation based on the multifractal theory. This study hence examines the feasibility of applying the multifractal theory to analyse the electricity price fluctuation, and applies the multifractal theory for evaluating the financial risk caused by electricity price volatility. A modified return interval approach considering the parameters of multifractal characteristics is employed to estimate the value‐at‐risk (VaR) of the electricity price. The fluctuant electricity price data series in the Pennsylvania‐New Jersey‐Maryland energy market are employed to demonstrate the effectiveness of the proposed VaR estimation method for short‐term electricity price volatility risk evaluation. [ABSTRACT FROM AUTHOR]

Energy use is on the rise due to an increase in the number of households and general consumptions. It is important to estimate and forecast the number of houses and the resultant energy consumptions to address the effective and efficient use of energy in future planning. In this paper, the number of houses in Brunei Darussalam is estimated by using Spline interpolation and forecasted by using two methods, namely an autoregressive integrated moving average (ARIMA) model and nonlinear autoregressive (NAR) neural network. The NAR model is more accurate in forecasting the number of houses as compared to the ARIMA model. The energy required for water heating and other appliances is investigated and are found to be 21.74% and 78.26% of the total energy used, respectively. Through analysis, it is demonstrated that 9 m2 solar heater and 90 m2 of solar panel can meet these energy requirements. [ABSTRACT FROM AUTHOR]