Real-time Electricity Market Research Articles

Demand Response Management for Profit Maximizing Energy Loads in Real-Time Electricity Market

In this paper, we consider the profit-maximizing demand response of an energy load in the real-time electricity market. In a real-time electricity market, the market clearing price is determined by the random deviation of actual power supply and demand from the predicted values in the day-ahead market. An energy load, which requires a total amount of energy over a certain period of time, has the flexibility of shifting its energy usage in time, and therefore is in perfect position to exploit the volatile real-time market price through demand response. We show that the profit-maximizing demand response strategy can be obtained by solving a finite-horizon Markov decision process (MDP) problem, which requires extremely high computational complexity due to continuous state and action spaces. To tackle the high computational complexity, we propose a dual approximate approach that transforms the MDP problem into a linear programing problem by exploiting the threshold structure of the optimal solution. Then, a row-generation-based solution algorithm is proposed to solve the problem efficiently. We demonstrate through extensive simulations that the proposed method significantly reduces the computational complexity of the optimal MDP problem (linear versus exponential complexity), while incurring marginal performance loss. More interestingly, the proposed demand response strategy hits a triple win. It not only maximizes the profit of the energy load, but also alleviates the supply-demand imbalance in the power grid, and even reduces the bills of other market participants. On average, the proposed quadratic approximation and improved row generation algorithm increases the energy load's profit by 55.9% and saves the bills of other utilities by 80.2% comparing with the benchmark algorithms.

SF-OEAP: Starvation-Free Optimal Energy Allocation Policy in a Smart Distributed Multimicrogrid System

The advancements in the smart grid involves high penetration of renewable energy resources into distribution system by building a microgrid. However, the intermittent nature of the nondispatchable generation leads to the issue of demand supply mismatch in the microgrid system. It will be sensible for microgrids to trade energy with one another. This requires a smart distributor, which collects data regarding superfluous energy from providers and takes fair decision of microgrid-generated energy allocation among consumers. This paper presents a starvation-free optimal microgrid-generated energy allocation policy (SF-OEAP) in a smart distributed multimicrogrid system. A novel optimal energy allocation policy is proposed wherein, prediction accuracy of net load forecast and revenue index of consumer microgrid are considered as vital parameters. In a prioritized energy distribution mechanism, it may happen that the consumer microgrid can be starved of microgrid-generated energy. Therefore, in addition to vital parameters, starvation parameter is considered in designing energy allocation policy for sustaining the consumers in the smart distributed multimicrogrid system. Furthermore, the proposed scheme uses weights to control the importance of each of the parameters in the energy allocation policy. The proposed SF-OEAP is simulated and justified so that it is stable enough to apply in a real-time electricity market.

A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

This paper applies a multi-factor, stochastic latent moment model to predicting the imbalance volumes in the Austrian zone of the German/Austrian electricity market. This provides a density forecast whose shape is determined by the flexible skew-t distribution, the first three moments of which are estimated as linear functions of lagged imbalance and forecast errors for load, wind and solar production. The evaluation of this density predictor is compared to an expected value obtained from OLS regression model, using the same regressors, through an out-of-sample backtest of a flexible generator seeking to optimize its imbalance positions on the intraday market. This research contributes to forecasting methodology and imbalance prediction, and most significantly it provides a case study in the evaluation of density forecasts through decision-making performance. The main finding is that the use of the density forecasts substantially increased trading profitability and reduced risk compared to the more conventional use of mean value regressions.

Carbon trading’s impact on California’s real-time electricity market prices

What is the extent of a real-time electricity market’s pass-through of the marginal cost of CO2 emissions due to a cap-and-trade (C&T) program? This is an important policy question, as an incomplete pass-through would suggest the program’s limited effectiveness in achieving efficient pricing of electricity. To answer the question, we perform a regression analysis of California’s electricity market data for a 65-month period of 01/01/2011–05/31/2016. Based on this newly constructed large sample, we find that the California Independent System Operator’s real-time market prices contain a CO2 premium that closely tracks the marginal cost of CO2 emissions of California’s natural-gas-fired generation units, which are often at margin that determines the power prices. While the CO2 premium provides much needed incentives for renewable energy development, it does little to improve the incentive for natural-gas-fired generation investment in California. Hence, procurement of dispatchable generation capacity via long-term contracts continues to be useful for the state to meet the mandatory criteria for resource adequacy and system reliability.

A demand response system for wind power integration: greenhouse gas mitigation and reduction of generator cycling

A smart grid power system for a small region consisting of 1,000 residential homes with electric heating appliances from the demand side, and a generic generation mix of nuclear, hydro, coal, gas and oil-based generators representing the supply side, is investigated using agent-based simulations. The simulation includes a transactive load control in a real-time pricing electricity market. The study investigates the impacts of adding wind power and demand response (DR) on both greenhouse gas (GHG) emissions and generator cycling requirements. The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation. The extent to which greenhouse gas emissions can be mitigated is found to be highly dependent on the mix of generators and their operational capacity factors. It is expected that the effects of demand response on electricity use can reduce dependency on fossil fuel-based electricity generation. However, the anticipated mitigation of GHG emissions is found to dependent on the number and efficiency of fossil fuel generators, and especially on the capacity factor at which they operate. Therefore, if a generator (the marginal seller) is forced to use less efficient fossil fuel power generation schemes, it will result in higher GHG emissions. The simulations show that DR can yield a small reduction in GHG emissions, but also lead to a smaller increase in emissions in circumstances when, for example, a generator (the marginal seller) is forced to use less efficient fossil fuel power generation schemes. Nonetheless, DR is shown to enhance overall system operation, particularly by facilitating increased penetration of variable renewable electricity generation without jeopardizing grid operation reliability. DR reduces the amount of generator cycling by an increased order of magnitude, thereby reducing wear and tear, improving generator efficiency, and avoiding the need for additional operating reserves. The effectiveness of DR for these uses depends on the participation of responsive loads, and this study highlights the need to maintain a certain degree of diversity of loads to ensure they can provide adequate responsiveness to the changing grid conditions.

Optimal Scheduling for Prosumers in Coupled Transactive Power and Gas Systems

To help the integration of various distributed energy resources (DERs), this paper proposes a transactive approach to the optimal scheduling for prosumers in coupled energy systems. DERs are coordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity market, as well as the wholesale gas market. In the day-ahead electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time balancing market, a VPP adjusts DER schedules to minimize imbalance costs. In this paper, we address the energy conversions between electric power and gas loads and investigate the interacting operations of gas and power systems. Our simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy systems.

Electricity Cost Minimization for Delay-tolerant Basestation Powered by Heterogeneous Energy Source

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day’s electricity cost of individual BS.

Distributed Optimal Power Management via Negawatt Trading in Real-Time Electricity Market

This paper presents a distributed optimal power supply–demand management method by using negawatt trading in a real-time electricity market. In the negawatt trading, power consumers reduce their own power demand and receive monetary reward from the market operator. This paper discusses a power adjustment method via negawatt trading by including charge or discharge power from energy storage systems. In order to derive appropriate electricity incentive prices for consumers and to use energy storage systems in power grids efficiently, we deal with a power adjustment problem by using some future power supply information from generators. We then show a novel market algorithm to solve this problem in a distributed manner through market trading. Finally, numerical simulation results illustrate the effectiveness of our proposed method.

Robust Co-Optimization to Energy and Reserve Joint Dispatch Considering Wind Power Generation and Zonal Reserve Constraints in Real-Time Electricity Markets

This paper proposes an energy and reserve joint dispatch model based on a robust optimization approach in real-time electricity markets, considering wind power generation uncertainties as well as zonal reserve constraints under both normal and N-1 contingency conditions. In the proposed model, the operating reserves are classified as regulating reserve and spinning reserve according to the response performance. More specifically, the regulating reserve is usually utilized to reduce the gap due to forecasting errors, while the spinning reserve is commonly adopted to enhance the ability for N-1 contingencies. Since the transmission bottlenecks may inhibit the deliverability of reserve, the zonal placement of spinning reserve is considered in this paper to improve the reserve deliverability under the contingencies. Numerical results on the IEEE 118-bus test system show the effectiveness of the proposed model.

Stochastic Interchange Scheduling in the Real-Time Electricity Market

The problem of multi-area interchange scheduling in the presence of stochastic generation and load is considered. A new interchange scheduling technique based on a two-stage stochastic minimization of overall expected operating cost is proposed. Because directly solving the stochastic optimization is intractable, an equivalent problem that maximizes the expected social welfare is formulated. The proposed technique leverages the operator's capability of forecasting locational marginal prices (LMPs) and obtains the optimal interchange schedule without iterations among operators.

Coordinated multi-area energy and regulation joint dispatch under wind power uncertainty

Proliferated integration of variable renewable resources, especially wind power, in multi-area power systems brings about critical needs to coordinate balancing resources over the whole interconnected system. This paper proposes a coordination methodology in the real-time electricity market between regional interconnected power systems. The new method optimally dispatches generating units, determines interregional energy trading, and allocates regulation reserve throughout the entire system. The balancing resources in participating areas are shared and more effectively used to tackle the wind power uncertainty by means of a combined automatic generation control system. In order to comply with the existing operating philosophy of regional electricity markets, the augmented Lagrangian relaxation along with alternative direction method of multipliers are applied to provide a fully decentralized procedure with limited data interchanges between neighboring regions. Performance of the proposed method is examined on a two-area 6-bus and the modified three-area IEEE 118-bus test systems. The results confirm the applicability and effectiveness of the proposed model in providing flexible energy trading, regulation sharing, improved economic efficiency, and increased ability to integrate variable renewable energy into the power systems.

A Dynamic Penalty Cost Allocation Based Uncertain Wind Energy Scheduling in Smart Grid

The intermittent nature of wind energy conversion presents a risk to the Independent System Operator (ISO) in a real time electricity market. Consequently, there is a need to appropriately incorporate this risk in the wind energy scheduling paradigm. In the present work, the intermittency associated risk has been modeled as part of a cost optimization problem for the ISO in real time. A model to minimize the risk has also been proposed using various cost models that use dynamic risk aversion costs and reflect the market and system operating conditions. The two dynamic penalty cost/risk models included are, rescheduling cost and contractual compensation cost for wind energy deviation. The results obtained are compared with those from the deterministic model. The proposed approach is simulated on the IEEE 30 bus system and the findings from the proposed approach for wind energy scheduling lead to a low operational cost to the ISO in the real time market considered in the study. Among other observations, the consideration of uncertainty in Day-Ahead market leads to increase in cost savings of ISO with increase in wind uncertainty, but a corresponding reduction in the scheduled wind energy in the same market.

An Integrated eVoucher Mechanism for Flexible Loads in Real-Time Retail Electricity Market

This paper proposes an innovative economic and engineering coupled framework to encourage typical flexible loads or load aggregators, such as parking lots with high penetration of electric vehicles, to participate directly in the real-time retail electricity market based on an integrated eVoucher program. The integrated eVoucher program entails demand side management, either in the positive or negative direction, following a popular customer-centric design principle. It provides the extra economic benefit to end-users and reduces the risk associated with the wholesale electricity market for electric distribution companies (EDCs), meanwhile improving the potential resilience of the distribution networks with consideration for frequency deviations. When implemented, the eVoucher program allows typical flexible loads, such as electric vehicle parking lots, to adjust their demand and consumption behavior according to financial incentives from an EDC. A distribution system operator (DSO) works as a third party to hasten negotiations between such parking lots and EDCs, as well as the price clearing process. Eventually, both electricity retailers and power system operators will benefit from the active participation of the flexible loads and energy customers.

Optimizing Greenhouse Lighting for Advanced Agriculture Based on Real Time Electricity Market Price

The world’s growing demand for food can be met by agricultural technology. Use of artificial light to supplement natural sunlight in greenhouse cultivation is one of the most common techniques to increase greenhouse production of food crops. However, artificial light requires significant electrical energy, which increases the cost of greenhouse production and can reduce profit. This paper models the increments to greenhouse productivity as well as the increases in cost from supplemental electric lighting, in a situation where the greenhouse is one of the elements of a smart grid, a system where the electric energy market is dynamic and prices vary over time. We used our models to calculate the optimum values for supplemental light and the required electrical energy for HPS lamps in the greenhouse environment, using cherry tomato cultivation as a case study crop. We considered two optimization techniques: iterative search (IS) and genetic algorithm (GA). The two approaches produced similar results, although the GA method was much faster. Both approaches verify the advantages of using optimal supplemental light in terms of increasing production and hence profit.

Robust Co-Optimization to Energy and Ancillary Service Joint Dispatch Considering Wind Power Uncertainties in Real-Time Electricity Markets

This paper presents an adjustable robust co-optimization model for energy and ancillary service markets joint dispatch considering wind power uncertainties in real-time electricity markets. The participation factors are employed to generate adjustable generation outputs and ensure energy balance. The proposed robust optimization model can be equivalently transformed into a large-scale linear programming model with a huge number of dummy variables and additional constraints that challenge real-time markets. To further reduce the computation burden, the redundant constraint reduction strategy is proposed to fast eliminate redundant transmission security constraints before solving the model. The results from the IEEE 30-bus system with two wind farms show that the proposed robust joint optimization model provides a better solution compared to those cases using sequential dispatch or deterministic dispatch methodologies. Additional results from practical large-scale systems suggest that the number of redundant constraints is less than 6%, indicating the effectiveness of the proposed redundant constraint reduction strategy in improving the computation efficiency of the robust joint dispatch in real-time markets.

Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

Grid-scale battery energy storage systems (BESSs) are promising to solve multiple problems for future power systems. Due to the limited lifespan and high cost of BESS, there is a cost-benefit trade-off between battery effort and operational performance. Thus, we develop a battery degradation model to accurately represent the battery degradation and related cost during battery operation and cycling. A linearization method is proposed to transform the developed battery degradation model into the mixed integer linear programming (MILP) optimization problems. The battery degradation model is incorporated with a hybrid deterministic/stochastic look-ahead rolling optimization model of wind-BESS bidding and operation in the real-time electricity market. Simulation results show that the developed battery degradation model is able to effectively help to extend the battery cycle life and make more profits for wind-BESS. Moreover, the proposed rolling look-ahead operational optimization strategy can utilize the updated wind power forecast, thereby also increase the wind-BESS profit.

Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market

District energy systems can produce low-cost utilities for large energy networks, but can also be a resource for the electric grid by their ability to ramp production or to store thermal energy by responding to real-time market signals. In this work, dynamic optimization exploits the flexibility of thermal energy storage by determining optimal times to store and extract excess energy. This concept is applied to a polygeneration distributed energy system with combined heat and power, district heating, district cooling, and chilled water thermal energy storage. The system is a university campus responsible for meeting the energy needs of tens of thousands of people. The objective for the dynamic optimization problem is to minimize cost over a 24-h period while meeting multiple loads in real time. The paper presents a novel algorithm to solve this dynamic optimization problem with energy storage by decomposing the problem into multiple static mixed-integer nonlinear programming (MINLP) problems. Another innovative feature of this work is the study of a large, complex energy network which includes the interrelations of a wide variety of energy technologies. Results indicate that a cost savings of 16.5% is realized when the system can participate in the wholesale electricity market.

Zonal merit-order effects of wind generation development on day-ahead and real-time electricity market prices in Texas

This paper uses a regression-based approach to explore the impact of wind generation development on wholesale electricity prices in the Electric Reliability Council of Texas (ERCOT) market. We find that wind generation development has a greater effect on real-time market (RTM) prices than day-ahead market (DAM) prices. Higher wind generation forecast errors tend to reduce the RTM prices, chiefly because unanticipated increases in wind generation reduce the real-time net loads to be served by fossil fuel power plants. Improving ERCOT’s load and wind generation forecast accuracy tends to make the DAM and RTM prices converge, thus enhancing ERCOT’s market trading efficiency. Finally, the estimated merit-order effects are greatest in the ERCOT zones where the wind generation capacity locally resides.

Instrumentation Strategies and Instrument Mixes for Long Term Climate Policy

The good intentions of COP21 are to be reached by novel climate policy instrumentation, beyond what has been developed till now. For the long term two fundamentally different strategies emerge, each leading to a set of instruments fit for the challenging task in principle. The first has a command and control orientation, developing and specifying the technologies required and bringing them in the market. Prime instruments are subsidies like feed-in tariffs and capital subsidies and standards and regulations, as to force coal fired power stations out of the market and efficient cars into the market. These technology specific instruments override the current strict EU cap-and-trade system. This governance orientation is on optimization from a broad and integrative perspective, including distributional issues, energy policy and transport policy. The second strategy has its focus on incentive creation and option creation, transforming and strengthening EU ETS into a price stabilized emission pricing system, ultimately as an emission tax. A core institutional element is the creation of a European open real time electricity market, with equal but quite variable prices for all primary and secondary producers and for all users. Two sets of instruments are substantially mutually exclusive; feed-in tariffs and user differentiated surcharges to fund them are not compatible with open electricity markets. The two sets of instruments are substantially mutually exclusive; for example feed-in tariffs and user differentiated surcharges to fund them are not compatible with open electricity markets and not compatible with pure cap-and-trade systems. Discussion and choices are due.

Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming

There is growing interest in the use of grid-level storage to smooth variations in supply that are likely to arise with an increased use of wind and solar energy. Energy arbitrage, the process of buying, storing, and selling electricity to exploit variations in electricity spot prices, is becoming an important way of paying for expensive investments into grid-level storage. Independent system operators such as the New York Independent System Operator (NYISO) require that battery storage operators place bids into an hour-ahead market (although settlements may occur in increments as small as five minutes, which is considered near “real-time”). The operator has to place these bids without knowing the energy level in the battery at the beginning of the hour and simultaneously accounting for the value of leftover energy at the end of the hour. The problem is formulated as a dynamic program. We describe and employ a convergent approximate dynamic programming (ADP) algorithm that exploits monotonicity of the value function to find a revenue-generating bidding policy; using optimal benchmarks, we empirically show the computational benefits of the algorithm. Furthermore, we propose a distribution-free variant of the ADP algorithm that does not require any knowledge of the distribution of the price process (and makes no assumptions regarding a specific real-time price model). We demonstrate that a policy trained on historical real-time price data from the NYISO using this distribution-free approach is indeed effective.