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The distribution company (DISCO) determines optimal retail prices to operate the distribution network efficiently while promoting demand response (DR) programs. In addition, an energy storage system (ESS), which improves peak load management, is widely used for price-based DR. This paper proposes an electricity retail pricing strategy that considers the optimal operation of an ESS using a machine learning algorithm. An artificial neural network (ANN) is used to develop a practical model of the DR scheduling of an ESS. This model is trained using historical data that include the electricity price and the corresponding optimal demand obtained from the building energy management system. The proposed model is replicated using mathematical equations and directly integrated into the constraints of the retail pricing optimization problem of the distribution management system. The proposed ANN-based DR model of the ESS allows the development of an optimal pricing strategy with a single-level structure while reflecting the decision-making process of both the DISCO and the building operator. The proposed ANN-based DR model is verified through case studies, which prove that the model successfully expresses the price-optimal demand function and has high practical applicability. The results of the retail pricing demonstrate that the proposed strategy can accurately determine the balancing points while reducing the peak load.

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

This paper presents a voltage control method using multiple distributed generators (DGs) based on a multi-agent system framework. The output controller of each DG is represented as a DG agent, and each voltage-monitoring device is represented as a monitoring agent. These agents cooperate to accomplish voltage regulation through a coordinating agent or moderator. The moderator uses the reactive power sensitivities and margins to determine the voltage control contributions of each DG. A fuzzy inference system (FIS) is employed by the moderator to manage the decision-making process. An FIS scheme is developed and optimized to enhance the efficiency of the proposed voltage control process using particle swarm optimization. A simple distribution system with four voltage-controllable DGs is modeled, and an FIS moderator is implemented to control the system. Simulated data show that the proposed voltage control process is able to maintain the system within the operating voltage limits. Furthermore, the results were similar to those obtained using optimal power flow calculations, even though little information on the power system was required and no power flow calculations were implemented.

Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.

A distribution system was designed and operated by considering unidirectional power flow from a utility source to end-use loads. The large penetrations of distributed generation (DG) into the existing distribution system causes a variety of technical problems, such as frequent tap changing problems of the on-load tap changer (OLTC) transformer, local voltage rise, protection coordination, exceeding short-circuit capacity, and harmonic distortion. In view of voltage regulation, the intermittent fluctuation of the DG output power results in frequent tap changing operations of the OLTC transformer. Thus, many utilities limit the penetration level of DG and are eager to find the reasonable penetration limits of DG in the distribution system. To overcome this technical problem, utilities have developed a new voltage regulation method in the distribution system with a large DG penetration level. In this paper, the impact of DG on the OLTC operations controlled by the line drop compensation (LDC) method is analyzed. In addition, a generalized determination methodology for the DG penetration limits in a distribution substation transformer is proposed. The proposed DG penetration limits could be adopted for a simplified interconnection process in DG interconnection guidelines.

Low-voltage direct current (LVDC) distribution systems have been evolving into interesting ways of integrating distributed energy resources (DERs) and power electronics loads to local distribution networks. In LVDC distribution systems, voltage regulation is one of the most important issues, whereas AC systems have concerns such as frequency, power factor, reactive power, harmonic distortion and so on. This paper focuses on a voltage control method for a LVDC distribution system based on the concept of multi-agent system (MAS), which can deploy intelligence and decision-making abilities to local areas. This paper proposes a distributed power flow analysis method using local information refined by local agents and communication between agents based on MAS. This paper also proposes a voltage control method by coordinating the main AC/DC converter and multiple DERs. By using the proposed method, we can effectively maintain the line voltages in a pre-defined normal range. The performance of the proposed voltage control method is evaluated by case studies and compared to conventional methods.

A distribution system was designed and operated by considering unidirectional power flow from a utility source to end-use loads. The large penetrations of distributed generation (DG) into the existing distribution system causes a variety of technical problems, such as frequent tap changing problems of the on-load tap changer (OLTC) transformer, local voltage rise, protection coordination, exceeding short-circuit capacity, and harmonic distortion. In view of voltage regulation, the intermittent fluctuation of the DG output power results in frequent tap changing operations of the OLTC transformer. Thus, many utilities limit the penetration level of DG and are eager to find the reasonable penetration limits of DG in the distribution system. To overcome this technical problem, utilities have developed a new voltage regulation method in the distribution system with a large DG penetration level. In this paper, the impact of DG on the OLTC operations controlled by the line drop compensation (LDC) method is analyzed. In addition, a generalized determination methodology for the DG penetration limits in a distribution substation transformer is proposed. The proposed DG penetration limits could be adopted for a simplified interconnection process in DG interconnection guidelines. [ABSTRACT FROM AUTHOR]