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Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS). [ABSTRACT FROM AUTHOR]

Through the application of flexible Time-of-Use (ToU) tariffs, demand side management (DSM) can be facilitated in order to alleviate grid congestion problems and potential network reinforcement. In this work, a novel approach to derive ToU tariffs for residential prosumers is described and the potential impact is verified in a pilot network within the distribution grid of Cyprus comprised of three hundred prosumers. This pilot network acts as a test-bed for defining the baseline scenario and subsequently verifying the developed ToU tariffs. The ToU block periods were determined by combining statistical analysis and a hybrid optimization function that utilizes annealing driven pattern search algorithms. The ToU rates were calculated by exploiting an optimization function that maintained a neutral electricity bill in the case where the load profile remained unchanged. The impact of the derived ToU tariffs was first analysed through a sensitivity analysis performed on the seasonal load profiles of the participants. The obtained sensitivity analysis results showed that for the summer and winter season, the maximum Load Factor (LF) was 42.83% and 33.33% respectively and occurred when load was shifted mainly to the off-peak period. Finally, with the ToU tariffs applied to the pilot network of prosumers, the effectiveness and potential response of the prosumers to the imposed tariffs, was verified. The results indicated that the LF was increased while the percentage of total consumption measured during peak hours was reduced by 3.19%, 1.03% and 1.40% for the summer, middle and winter season respectively. This led to the conclusion that the derived ToU tariffs are effective in persuading the prosumers to change their energy behaviour. [ABSTRACT FROM AUTHOR]

Load participation is vital for the smart grid development. As an effective tool to improve reliability, stability, and financial efficiency of the power grids, demand response (DR) has brought significant financial and technical benefits to power systems. As one of the price-based DR programs with less control costs, the time-of-use (TOU) program has been applied as the default rate structure by many utility companies. To avoid financial risks and make the most profit from the market, utility companies treat TOU as an effective strategy to change customers’ electricity consumption patterns. As reported in many literature, existing TOU programs are not as effective as expected in many developed countries due to the complexity of human behaviors and disparities of residential customers. To examine whether to obtain different outcomes of TOU on the residential customers in developing countries, actual utility usage data from residential consumers in Shanghai, China, are analyzed in this paper. The result shows current TOU in Shanghai, China, has similar trends as TOU in developed countries. With high penetration level of renewable energy, an effective TOU program is urgently needed in the utility industry. In recent years, a creative TOU pricing structure has been introduced at the Electrical Reliability Council of Texas deregulated market, and it shows that the introduced “zero pricing” strategy has a significant impact on customers’ consumption patterns. The purpose of this research is to examine the key reasons that underlie ineffectiveness/effectiveness of TOU programs at residential level. [ABSTRACT FROM PUBLISHER]

Time-of-use price will be an important demand side management method as a supplement of administrative strategy, which could encourage the electric vehicles to change the charging behaviors. In this paper, for parking garage charging stations with photovoltaic power generation, a pricing model is proposed to encourage different consumers to participate in demand response by providing them with a list of price plans. Firstly, we introduce the system structure of the photovoltaic-assisted charging station. Afterwards, the pricing and dispatch model for the charging station is proposed, aiming to achieve maximum profit. The formulated objective for this model is to maximize the profit of selling electricity to electric vehicles and back to the grid, feed-in-tariffs revenue and parking revenue or to minimize charging station's cost of purchasing electricity, while considering various electric vehicles demands. The decision variables for charging station include the time-of-use price, the rebate on parking fee and the charging/discharging power. To solve this model, the distributed alternating direction method of multipliers is adopted to determine the operational strategies of charging stations via local decision making and limited communication between adjacent charging stations. Finally, according to the simulation results, the optimal time-of-use pricing strategy for charging stations can play an important role in shaving the peak loads. Besides, the interconnected charging stations coordinated operation can enhance the overall system operational flexibility and reliability. [ABSTRACT FROM AUTHOR]

Demand response (DR) is a useful tool for end users, since it allows noticeable reductions in the electricity bill. However, some customers have stringent constraints in terms of hourly active power, which makes DR attractive only when performed with the contemporaneous use of battery energy storage systems (BESSs). When this option is used, it is desirable to optimally size the BESSs, since their high investment costs could make their use impracticable for DR purposes. The uncertainties related to many of the inputs required by the BESS-sizing procedure make it necessary to use a probabilistic framework for sizing. In this paper, a probabilistic approach is proposed for the optimal sizing of BESSs when time-of-use (ToU) pricing is applied. The sizing procedure takes into account the uncertainties that unavoidably affect the evaluation of the total cost incurred by the customer such as load demand, energy prices, and economic factors. [ABSTRACT FROM AUTHOR]

For decades, electricity prices for larger commercial and industrial customers have included demand charges, which recover a portion of the revenue requirement based on the customer's highest usage during the month. Data being collected through smart meters allows utilities to consider expanding the use of demand charges to residential consumers. [ABSTRACT FROM AUTHOR]

In deregulated and smart grid system, time based tariff is implemented using complex communication system. This time based tariff depending on market clearing price initiates demand management. Moreover, the consumer supporting the utility by adopting demand management technique is not benefited directly by the utility. In this paper, tariff is locally decided by the smart meter based on change in frequency and change in demand using Fuzzy logic. Based on the relation between change in frequency and change in demand with tariff, fuzzy rules are developed. On implementing fuzzy based tariff, the consumers supporting utility by doing demand management gets more benefited without using the complex communication network. [ABSTRACT FROM AUTHOR]

Demand for electricity varies throughout the day, increasing the average cost of power supply. Time-of-use (TOU) pricing has been proposed as a demand-side management (DSM) method to influence user demands. In this paper, we describe a game-theoretic approach to optimize TOU pricing strategies (GT-TOU). We propose models of costs to utility companies arising from user demand fluctuations, and models of user satisfaction with the difference between the nominal demand and the actual consumption. We design utility functions for the company and the users, and obtain a Nash equilibrium using backward induction. In addition to a single-user-type scenario, we also consider a scenario with multiple types of users, each of whom responds differently to time-dependent prices. Numerical examples show that our method is effective in leveling the user demand by setting optimal TOU prices, potentially decreasing costs for the utility companies, and increasing user benefits. An increase in social welfare measure indicates improved market efficiency through TOU pricing. [ABSTRACT FROM PUBLISHER]

Abstract: In the electricity industry, the processes through which consumers respond to price signals embedded in tariffs by changing their consumption patterns is generally referred to as demand response. In such a context, consumers are offered an opportunity to maximize the surplus derived from electricity usage by actively scheduling consumption over time periods with potentially different energy prices. The objective of this work is to analyze the role of correlation in prices of successive periods over which consumption is to be scheduled, in a demand response context. We use robust optimization techniques to propose an optimization model for consumption scheduling when prices of different periods are highly correlated, and also suggest approaches to correctly incorporate real-world correlated price data to this model. Positive results from quantitative case studies indicate that it is of great importance to employ a solution approach that correctly models correlation among prices when scheduling consumption. [Copyright &y& Elsevier]

Abstract: Assessing the impacts of dynamic-pricing under the smart grid concept is becoming extremely important for deciding its full deployment. In this paper, we develop a model that represents the response of consumers to dynamic pricing. In the model, consumers use forecasted day-ahead prices to shift daily energy consumption from hours when the price is expected to be high to hours when the price is expected to be low while maintaining the total energy consumption as unchanged. We integrate the consumer response model into the Electricity Market Complex Adaptive System (EMCAS). EMCAS is an agent-based model that simulates restructured electricity markets. We explore the impacts of dynamic-pricing on price spikes, peak demand, consumer energy bills, power supplier profits, and congestion costs. A simulation of an 11-node test network that includes eight generation companies and five aggregated consumers is performed for a period of 1 month. In addition, we simulate the Korean power system. [Copyright &y& Elsevier]

One of the most common demand side management programs consists of time-of-use (TOU) tariffs, where consumers are charged differently depending on the time of the day when they make use of energy services. This paper assesses the impacts of TOU tariffs on a dataset of residential users from the Province of Trento in Northern Italy in terms of changes in electricity demand, price savings, peak load shifting and peak electricity demand at sub-station level. Findings highlight that TOU tariffs bring about higher average electricity consumption and lower payments by consumers. A significant level of load shifting takes place for morning peaks. However, issues with evening peaks are not resolved. Finally, TOU tariffs lead to increases in electricity demand for substations at peak time. [ABSTRACT FROM AUTHOR]

We consider interruptible electricity contracts issued by an electricity retailer that allow for interruptions to electric service in exchange for either an overall reduction in the price of electricity delivered or for financial compensation at the time of interruption. We provide a structural model to determine electricity prices based on stochastic models of supply and demand. We use stochastic dynamic programming to value interruptible contracts from the point of view of an electricity retailer, and describe the optimal interruption strategy. We also demonstrate that structural models can be used to value contracts in competitive markets. Our numerical results indicate that, in a deregulated market, interruptible contracts can help alleviate supply problems associated with spikes of price and demand and that competition between retailers results in lower value and less frequent interruption. [ABSTRACT FROM AUTHOR]

Retail real-time pricing (RTP) of electricity -- retail pricing that changes hourly to reflect the changing supply/demand balance -- is very appealing to economist because it "sends the right price signals." Economic efficiency gains from RTP. however, are often confused with the short-term wealth transfers from producers to consumers that RTP can create. Abstracting from transfers, I focus on the long-run efficiency gains from adopting RTP in a competitive electricity market. Using simple simulations with realistic parameters, I demonstrate that the magnitude of efficiency gains from RTP is likely to be significant even if demand shows very little elasticity. I also show that "time-of-use" pricing, a simple peak and off-peak pricing system, is likely to capture a very small share of the efficiency gains that RTP offers. [ABSTRACT FROM AUTHOR]

An effective national energy policy must include a broad array of approaches to meeting the nation's energy needs. The more energy arrows in the quiver, so to speak, the better we may meet the challenge. As it has evolved over the past 25 years, our energy policy has reflected mounting awareness that we must address both the need to increase energy supply and the need to curb the growth in energy demand. Yet the application of attention and resources to these complementary efforts has been uneven. Energy supply issues are often at the forefront of political discourse and public awareness, and supply-side initiatives tend to generate the lion's share of funding. As to the various techniques of demand-side management (DSM), however, the pronouncements of policymakers have not always kept pace with technological advancements that stand to vastly improve the effectiveness of such techniques. Time-of-use energy pricing, in particular, could become a far more potent DSM tool as a result of innovations in metering technology. Despite the past relative neglect of such matters, there are encouraging signs in proposed federal legislation and ongoing federal and state regulatory initiatives that DSM in general, and time-of-use energy metering and pricing in particular, may finally have their day. [ABSTRACT FROM AUTHOR]