Home Energy Management (HEM) programs convince residential customers to actively participate in price-based demand response (DR) programs. In these price-oriented HEM methods, controller timing for energy consumption of home appliances in response to the electricity price signal has multiple priorities among customers. Although various methods have recently been proposed for the use of HEM, prioritizing the performance of controllable devices from the customer's perspective in price-oriented HEM has not been determined, and this issue is the focus of this paper. For this purpose, the value of lost load (VOLL) of each device is defined in such a way that it raises the implementation priority of that device from the customer's point of view. Considering the VOLL of the devices, electricity tariffs and implementation restrictions of the devices, the optimization problem is proposed to reduce the energy consumption and reliability prices. The result of the proposed HEM is the optimal timing of demand for household electricity. Numerical studies show the effectiveness of the proposed HEM method in a smart home considering different pricing based on variable time.

In this study, a new model is proposed to solve the problem of transmission and battery expansion planning considering integrated electricity and gas systems. The presented model is a bi-level stochastic planning model, where transmission and battery expansion planning modeling is done on one level, and gas network modeling is done on the other level. Here, the impact of the high penetration of renewable resources along with the possible event of electricity and gas network lines being out is also included in the problem of network expansion planning. The proposed model is a mixed integer linear programming model in both levels, the challenging solution of which is proposed using the KKT condition method with the help of the powerful Gurobi solver. Two experimental networks of 6 and 24 buses for the electricity network coupled with 5 and 10-node systems of the gas network are considered for analysis, and the results show the efficiency of the proposed model...

Privacy-preserving electricity markets have a key role in steering customers towards participation in local electricity markets by guarantying to protect their sensitive information. Moreover, these markets make it possible to statically release and share the market outputs for social good. This paper aims to design a market for local energy communities by implementing Differential Privacy (DP) that provably guarantees the privacy of market participants. Besides achieving a near-optimal solution and preserving the privacy of market participants, the proposed model maintains the utility of the market data for statistical releases. The required randomization for satisfying DP is embedded as Gaussian noise in each iteration of the gradient ascent algorithm underlying the optimization process of the market-clearing problem. In numerical studies, we investigate the impacts of the privacy loss parameter on the output distribution of the market-clearing quantities and payoffs of the market participants. In addition, we demonstrate the inherent trade-off between privacy protection and social welfare in the market under different privacy regimes.

Increasing the peak of electricity network in summer leads to power outages in industries and residential sectors, the most obvious example of which was power outages in the summer of 2018. Due to the high cost of power plant capacity development ($ 500 per kilowatt), demand-side management is the most important strategy to reduce the grid peak. In this study, the effect of behavioral parameters in reducing the peak of the power grid with the help of bottom-up modeling has been identified. Behavioral simulation in this study has been performed with the help of time use data of the Statistics Center of Iran. In this plan, 4228 urban households have been surveyed and the quality of people's behavior in each time step of 15 minutes during the day and night has been determined with 2 deterministic and stochastic approaches. In the stochastic approach, the Markov chain method is used. In this study, scenarios based on household comfort temperature have been developed. The results of the present study indicate that with the flexibility of cooling electricity consumption due to the presence of people in the house and the desired comfort temperature, it is possible to reduce the peak of the electricity network between 70 and 134 MW (equivalent to $ 65 million to build new power plant capacity). It is equal to 10% of the cooling peak of Tehran electricity network. With the spread of this 10% reduction to the whole country, about 6,000 MW (equivalent to $ 3 billion to create a new power plant capacity) the country's peak network has been reduced and the blackout crisis can be easily controlled.