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This paper introduces a framework to yield an electricity rate for vehicle-to-grid (V2G) charging station (CS) to minimize installation capacity of a charging station considering electric vehicle (EV) arrival/departure time distribution. Two different layers are designed to avoid an obstacle encountered when formulating the problem as a convex optimization and to represent an EV aggregator and an electricity rate decision maker – a regulator. The EV aggregator layer focuses on increasing the profit and the regulator minimizes the peak load of the V2G CS. Linear programming was formulated for the former layer, and a modified particle swarm optimization (PSO) method was developed for the latter. Modification of the PSO approach allowed for easier escape of local minima, resulting in a new electricity rate for the V2G CS based on the EV arrival/departure time distribution data. The algorithm employs new matrices devised in this paper to accommodate EV information in the optimization process. In a simulation study, two distinct CSs with V2G operations were evaluated, each with a different EV arrival/departure time distribution. The simulation revealed that the peak load and the profit of the aggregator vary dramatically depending on the arrival/departure time distributions.

To tackle the challenges associated with variability and uncertainty in distributed power generation, as well as the complexities of solving high-dimensional energy management mathematical models in mi-crogrid energy optimization, a microgrid energy optimization management method is proposed based on an improved soft actor-critic algorithm. In the proposed method, the improved soft actor-critic algorithm employs an entropy-based objective function to encourage target exploration without assigning signifi-cantly higher probabilities to any part of the action space, which can simplify the analysis process of distributed power generation variability and uncertainty while effectively mitigating the convergence fragility issues in solving the high-dimensional mathematical model of microgrid energy management. The effectiveness of the proposed method is validated through a case study analysis of microgrid energy op-timization management. The results revealed an increase of 51.20%, 52.38%, 13.43%, 16.50%, 58.26%, and 36.33% in the total profits of a microgrid compared with the Deep Q-network algorithm, the state-action-reward-state-action algorithm, the proximal policy optimization algorithm, the ant-colony based algorithm, a microgrid energy optimization management strategy based on the genetic algorithm and the fuzzy inference system, and the theoretical retailer stragety, respectively. Additionally, com-pared with other methods and strategies, the proposed method can learn more optimal microgrid energy management behaviors and anticipate fluctuations in electricity prices and demand.

Cryptocurrency has become a pivotal player in global financial transactions due to its decentralized mining process, which eliminates the need for intermediaries. However, the operation of cryptocurrency mining devices (CMDs) requires significant electrical power, leading to an increase in network-connected cryptocurrency mining loads (CMLs) that pose challenges for power grid operators. This paper evaluates the integration of CMLs into power grids, focusing on distribution network performance. A technology-accepted model for assessing cryptocurrency mining performance is presented, along with an analysis of the impacts of high CML penetration on power grid planning and operation. Findings reveal that high CML penetration can complicate load forecasting, congest transmission lines, and increase the coincident peak load in distribution networks. A case study of Iran’s power network indicates that CMLs account for approximately 2.8% of the nation’s electricity consumption. Furthermore, if 5% of residential customers in Tehran install CMDs, the average coincident peak demand for this group rises by 26.81%. Additionally, the average power factor of a sample CMD is about 0.99, while total current distortion and third harmonic current exceed permissible limits, indicating potential power quality issues. This study highlights the challenges posed by CMLs and proposes strategies for their sustainable integration into power grids.

• An optimization framework is introduced to determine the new electricity rate, considering demand-side power capacity limitations. • An EV connection matrix was developed to make charging/discharging scheduling algorithm implementation much easier. • The modified particle swarm optimization (PSO) algorithm increases the convergence rate of the algorithm. • Simulation results revealed that the TOU rates should be applied according to a specific pattern, depending on the location. This paper introduces a framework to yield an electricity rate for vehicle-to-grid (V2G) charging station (CS) to minimize installation capacity of a charging station considering electric vehicle (EV) arrival/departure time distribution. Two different layers are designed to avoid an obstacle encountered when formulating the problem as a convex optimization and to represent an EV aggregator and an electricity rate decision maker – a regulator. The EV aggregator layer focuses on increasing the profit and the regulator minimizes the peak load of the V2G CS. Linear programming was formulated for the former layer, and a modified particle swarm optimization (PSO) method was developed for the latter. Modification of the PSO approach allowed for easier escape of local minima, resulting in a new electricity rate for the V2G CS based on the EV arrival/departure time distribution data. The algorithm employs new matrices devised in this paper to accommodate EV information in the optimization process. In a simulation study, two distinct CSs with V2G operations were evaluated, each with a different EV arrival/departure time distribution. The simulation revealed that the peak load and the profit of the aggregator vary dramatically depending on the arrival/departure time distributions. [ABSTRACT FROM AUTHOR]

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Abstract Electricity industry restructuring in the United States, which began in the 1990s, was aimed at enhancing market competitiveness and lead to efficient resource allocation and cost reduction. The state of Delaware was one of the earliest adopters of restructuring and has gained more than 20 years of experience. The retail customers in a restructured market had greater opportunities to choose between electricity generators. However, this research compares electricity prices and finds that restructured states have higher electricity rates than non-restructured states. A better understanding of the welfare and satisfaction of electricity customers in the context of restructuring is needed. Highlights • Retail choice and lower prices were the purposes for electric market restructuring. • However, relatively high electricity price today in restructured states. • In some territories in Delaware, more retail choices, but few customers switched. • Customer protection and satisfaction policies are in demand. [ABSTRACT FROM AUTHOR]

This paper analyses both individual components of the price of electricity and the electricity rates as related to individual modes of heating. The price development has been monitored and assessed since July 1, 2001, i.e. the date on which the 'Price Decree of the Energy Regulation Office, No. 5/2001' entered into force concerning introduction of new rates and prices of electricity for households and businesses. Primarily, the rate, D 02d, was chosen for assessment. This rate is used by the majority of households as they use gas or solid fuels for heating. Further, the rate, D45 d, for direct electric heating, and rates, D55 d and D 56 d, for heating by thermal pumps were also subject of assessment. Both the development trends and the comparison of the investigated rates of electricity make it obvious that prices of electricity will continue to be rising in the long-term perspective.

The substantial increase in deployment of customer-sited solar photovoltaics (PV) in the United States has been driven by a combination of steeply declining costs, financing innovations, and supportive policies. Among those supportive policies is net metering, which in most states effectively allows customers to receive compensation for distributed PV generation at the full retail electricity price. The current design of retail electricity rates and the presence of net metering have elicited concerns that the possible under-recovery of fixed utility costs from PV system owners may lead to a feedback loop of increasing retail prices that accelerate PV adoption and further rate increases. However, a separate and opposing feedback loop could offset this effect: increased PV deployment may lead to a shift in the timing of peak-period electricity prices that could reduce the bill savings received under net metering where time-varying retail electricity rates are used, thereby dampening further PV adoption. In this paper, we examine the impacts of these two competing feedback dynamics on U.S. distributed PV deployment through 2050 for both residential and commercial customers, across states. Our results indicate that, at the aggregate national level, the two feedback effects nearly offset one another and therefore produce a modest net effect, although their magnitude and direction vary by customer segment and by state. We also model aggregate PV deployment trends under various rate designs and net-metering rules, accounting for feedback dynamics. Our results demonstrate that future adoption of distributed PV is highly sensitive to retail rate structures. Whereas flat, time-invariant rates with net metering lead to higher aggregate national deployment levels than the current mix of rate structures (+5% in 2050), rate structures with higher monthly fixed customer charges or PV compensation at levels lower than the full retail rate can dramatically erode aggregate customer adoption of PV (from −14% to −61%, depending on the design). Moving towards time-varying rates, on the other hand, accelerates near- and medium-term deployment (through 2030) but slows adoption in the longer term (−22% in 2050). [ABSTRACT FROM AUTHOR]

The effects of salmon recovery measures on the Northwest agricultural sector are evaluated. Relevant recovery measures, such as: modified timing for dam releases, reservoir drawdown, and flow augmentation in the Columbia River basin, on the regional agricultural sector are evaluated. Combined, these measures would increase power rates, grain transportation costs, and irrigation water costs and reduce the supply of water to irrigators. We quantify these input cost and quantity changes and combine them into seven recovery scenarios for analysis. Results suggest that drawdown and/or minor reductions in irrigation water diversions would reduce producers' profits by less than 1% of baseline levels. However, the most extreme scenario--a long drawdown period combined with a large reduction in irrigation diversions--would reduce producers' profits by $35 million (2.5%) annually. That effect is magnified at the local level; of the $35 million decline in annual profits, more than $27 million occur in southern Idaho and eastern Oregon. The federal government would bear these costs if it acquires water via voluntary transactions. [ABSTRACT FROM AUTHOR]