Stochastic Day-ahead Research Articles

Risk-aware day-ahead planning of an energy hub integrated with the carbon capture unit considering cap and trade concept, energy and carbon markets and demand side flexibility

This paper optimizes the stochastic day-ahead planning of an energy hub considering the carbon capture system, energy and carbon markets and flexible loads to achieve a sustainable economical-environmental operation. In the p÷resented model, the cap and trade concept as a successful policy in the regulatory programs of governments is considered to limit the level of environmental pollution caused by industries. In addition, various uncertainties including the power of wind and solar units, load demand and market prices are handled using scenario generation and reduction approaches. The downside risk method is also utilized to evaluate both risk-neutral and risk-averse attitudes of the decision-maker. In the following, the influence of demand side flexibility is analyzed by a time-of-use strategy for all types of loads. The results show that a risk-averse attitude with the maximum possible robustness increases the expected cost by about 19.55 %, while the expected risk reduces by about 100 %. The responsive loads notably can mitigate the impact of uncertainties, where their 10 % flexibility decreases the expected cost by about 19.02 %. The simulations validate the positive effect of carbon capture technology and the cap and trade concept on the environment; however, more strict rules and severe penalties are required.

Stochastic Day-Ahead Scheduling of Distributed Energy Resources: A Meta Modeling Approach

Stochastic Day-Ahead Scheduling of Distributed Energy Resources: A Meta Modeling Approach

Distributed risk-constrained strategy management of cloud energy storage for residential and electricity market services provision

Energy storage systems can improve the performance of home energy management systems and also provide ancillary services in electricity markets. However, the economic and technical insufficiency of individual home storage units, makes employing them inefficient for domestic storage and market applications, respectively. Therefore, central storage units with capacity sharing capability to small consumers as Cloud Energy Storages (CESs) are becoming a promising solution, and are also potentially able to contribute to ancillary service provision in the market. In this context, this paper aims to develop a novel stochastic day-ahead distributed management framework for CES to simultaneously adopt the optimal strategy of storage allocation for photovoltaic-integrated homes and participation in energy and ancillary service markets. The developed distributed framework is formulated within the Benders decomposition approach in which CES strategy and home energy management systems are optimized through master and subproblems based on linear mathematical models, respectively. Furthermore, the uncertainty of market prices is handled by the Conditional Value at Risk (CVaR) method. The developed model is assessed through real-world case studies of ERCOT. The results highlight the effectiveness of the collaborative service framework for both CES and households. Energy arbitrage through local storage services compensates for CES's challenges such as degradation and low power rating in the provision of energy services. These improvements benefit CES owners with higher net revenues and households with reduced cost of electricity supply.

Stochastic Day-ahead operation of cascaded hydropower systems with Bayesian neural network-based scenario generation: A Portland general electric system study

Coordinating water usage of cascaded dams along a river to serve electric power generation and other non-power needs is challenging, due to the volatile and time-variate streamflow, the tight hydrologic couplings between the dams, and the strict regulation of individual dams. With this, cascaded hydropower system (CHS) operators, such as Portland General Electric (PGE), usually adopt empirical strategies to sidestep these difficulties, but at the expense of water usage inefficiency and economic losses. Motived by this, a stochastic optimization-based day-ahead operation model of CHSs is developed to determine scenario-independent hourly forebay levels of individual dams, which could assist operators in effectively utilizing available water resources against inflow uncertainties and maximizing the profit of hydropower. A common penstock effect model is also integrated to accurately capture the physical operating characteristics of CHSs. Moreover, a data-driven Bayesian neural network (BNN)-based scenario generation model is seamlessly connected to the stochastic day-ahead operation model for preparing water inflow scenarios of the impending operating day. Leveraging actual operation data of the PGE’s Round Butte-Pelton CHS, numerical simulations demonstrate that the day-ahead operation schedules out of the proposed approach could deliver more efficient water usage and higher economic benefits than the PGE’s current practice.

Computational intelligence based PEVs aggregator scheduling with support for photovoltaic power penetrated distribution grid under snow conditions

• A snow loss-aware deep learning-based short-term PV yield predictor is developed. • Stochastic day-ahead scheduling of an independent PEVs aggregator is investigated. • A novel local balancing service offered by the PEVs aggregator to DSO is proposed. • The service benefits PEV owners through providing extra energy demand of the grid. This paper addresses the issue of optimal day-ahead scheduling of a plug-in electric vehicles (PEVs) aggregator that participates in the electricity market and offers an out-of-market balancing service to the local renewable power penetrated distribution system in a snow-prone area. The proposed balancing service provides a reliable source of flexibility for the extra real-time energy demand of the distribution system operator (DSO) which originates from the difference between its day-ahead bids and the actual demand. The problem is investigated on a snowy day when the DSO's day-ahead decisions encounter more uncertainty due to the considerable effect of snow loss on the DSO's photovoltaic plant performance. The aggregator's scheduling is formulated as two-stage stochastic programming which minimizes the PEVs’ charging cost. Monte Carlo simulation and K-means clustering are implemented to generate scenarios of driving patterns and real-time energy market prices, respectively. Offering the balancing service requires day-ahead predictions of the photovoltaic power and the grid load demand which are modeled using long short-term memory networks. The problem is formulated as mixed-integer linear programming. The results show that the proposed scheduling approach reduces the PEVs’ charging cost by 53% and guarantees the grid normal operation. Moreover, the balancing service can reduce the expected PEVs’ charging cost and the DSO's real-time cost by 12% and 14%, respectively. © 2017 Elsevier Inc. All rights reserved.

Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators

We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power scenarios by sampling from a conditional distribution that uses wind speed forecasts to tailor the scenarios to a specific day. We apply the generated scenarios in a stochastic day-ahead bidding problem of a wind electricity producer and analyze whether the scenarios yield profitable decisions. Compared to Gaussian copulas and Wasserstein-generative adversarial networks, the normalizing flow successfully narrows the range of scenarios around the daily trends while maintaining a diverse variety of possible realizations. In the stochastic day-ahead bidding problem, the conditional scenarios from all methods lead to significantly more stable profitable results compared to an unconditional selection of historical scenarios. The normalizing flow consistently obtains the highest profits, even for small sets scenarios.

Combined static and dynamic dispatch of integrated electricity and heat system: A real-time closed-loop demonstration

Coupling the electricity and heating systems introduce opportunities to handle the intermittent nature of an increasing share of renewable power generation. Coordination of units in the coupled systems requires careful static dispatch towards an optimization objective. As the uncertainties in renewable forecasting are reduced towards operation time, operators can perform dispatch numerous times to balance the determined mismatches. With higher shares of renewable, sudden changes in power generation due to changing weather can cause a power imbalance that disturbs the system frequency. To handle such hazards, dynamic dispatch in the form of frequency control is necessary. Recent literature in integrated electricity and heating systems (IEHS) is studied in this work to combine the static and dynamic dispatch of units. A two-stage stochastic day-ahead scheduling method combined with a model predictive control (MPC) -based real-time optimal operation method that allocates both following and regulating reserves is used as the static dispatch of the IEHS. The allocated reserves constrain the performance of the dynamic dispatch by limiting the control capacity. In this work, an MPC coordinated frequency control of small- and large-scale heat pumps is considered as the dynamic dispatch of the IEHS. The performance of the dynamic dispatch is evaluated through a real-time closed-loop simulation platform, where the system conditions of the static dispatch are used as the steady-state conditions. Results show that despite challenging communication conditions, the frequency control of small- and large-scale heat pumps improves frequency nadir conditions after a power imbalance.

Two-layer stochastic day-ahead and real-time energy management of networked microgrids considering integration of renewable energy resources

In this paper, we propose a multi-time scale energy management for the networked microgrids that considers both day-ahead and real-time scheduling. The proposed multi-time scale energy management system consists of two stages. In the first stage, a stochastic two-layer framework is implemented to minimize the operating costs of the networked microgrid in the day-ahead market. In the second stage, the first operation planning is updated in real-time every 15 min to reduce fluctuations in renewable energy sources. The objective function of the second stage is to minimize the cost of imbalance and optimally regulate the local generation of microgrids. Also, demand response programs have been used to reduce peak demand and the operating cost of MGs. Two case studies have been investigated to evaluate the impact of the proposed method. However, the effect of arbitrage in single and double price markets has been evaluated. The proposed model has been formulated as mixed-integer linear programming and the obtained results show that the proposed model reduces the operating cost by $86.27 and provides better planning by internal power trading.

Stochastic day-ahead optimal scheduling of multimicrogrids: an alternating direction method of multipliers (ADMM) approach

Multimicrogrid system is a novel notion in modern power systems as a result of developing renewable-based generation units and accordingly microgrids in distribution networks. Their energy management might be challenging due to presence of independent units. Thus, in this paper, a decentralized method for energy management of multimicrogrid systems has been proposed. Decentralized methods can enhance the privacy of users and reduce the burden of calculations. Alternating direction method of multipliers (ADMM) is selected as a decentralized approach which has the capability of breaking problems with complicating constraints in order to facilitate the solving process. Using decentralized approach not only reduces the burden of calculations, but also increases the privacy of entities. Wind turbines as renewable based generators are assumed to participate in this system. To model the uncertainties of these units, chance-constrained programming is employed. Also, due to clean output of hydrogen storage systems and fuel cells, the inclination for using these systems has expanded. Simulations on the test case study demonstrate the applicability and performance of the proposed methodology. Considering the reliability level as 0.9 results in 12, 989$ in the test case. By considering the reliability level as 0.8, the operational cost becomes 11, 712$ which shows a reduction of 1277$ which is achieved by jeopardizing the system reliability by 10%.

Demand response of grid-connected microgrid based on metaheuristic optimization algorithm

ABSTRACT Demand Response Programs (DRPs) have gained the microgrid (MG) operators phenomenal attention for mobilizing the end-users in alleviating the uncertainties associated with renewable energy sources. In this research work, the effective MG energy management system (EMS) in conjunction with price-driven DRPs is proposed to achieve synergistic coordination between the energy providers and consumers and reduce operational costs. The flexible price elasticity model is implemented instead of using the price elasticity models with a preordained constant value like most existing literature. This results in a more realistic characterization of customer responsiveness to energy price changes and promotes the DRPs under a grid-connected MG environment. With this regard, a stochastic day-ahead energy management strategy is proposed to incorporate four distinct DRPs and schedule the MG distributed energy resources. The proposed strategy is verified on a practical 3-feeder MG test system with a majority of 50% industrial load considered. The short-term scheduling period of 15 min ahead solar and wind power forecast is considered to optimize the microgrid dispatch costs accurately. The intermittent nature of renewable energy sources is addressed by employing a stochastic-based scenario generation and reduction approach. A novel and nature-inspired Black Widow Optimization (BWO) is applied to determine the optimal scheduling configuration. The effectiveness of the BWO is validated in terms of rate of convergence, computational time, and solution efficacy. Finally, the best DRP has been chosen based on its technical and economic performance indices by employing a multi-criteria decision-making approach.

Stochastic day-ahead scheduling of irrigation system integrated agricultural microgrid with pumped storage and uncertain wind power

Agricultural microgrid provides a promising solution for energy supply of rural areas in a cost-effective way. In this paper, the principle of wind-pumped storage integrated agricultural microgrid to fulfill both the electric and water load demand is explored. From the perspective of risk aversion, the indexes of expected power not served (EPNS) and expected power curtailment (EPC) are derived to evaluate the uncertain wind power. Then, a stochastic day-ahead scheduling model of wind-pumped storage power system and irrigation system is proposed to minimize the total operation cost whilst achieve the peak load shaving by taking the advantage of wind-pumped storage compensation. The proposed model is a complex optimization problem that takes into account distributed generations, electrical and water demand, turbine flow rate, and irrigation time and volume. We propose a intraspecific competition (IC) based evolutionary predator and prey strategy (EPPS) to solve the model. Finally, simulation results conducted on an agricultural microgrid have verified that, compared to previous works in the literature, the proposed model can effectively improve the utilization of wind power, reduce the operation cost of microgrid as well as smooth load curve. Additionally, the proposed algorithm can get a more competitive solution than other recently developed algorithms.

An adaptive real‐time energy management system for a renewable energy‐based microgrid

This paper proposes an adaptive real-time energy scheduling method (RT-EMS) for a microgrid, using a Lyapunov optimization-based real-time approach. Inaccuracy in day-ahead predictions can result in non-optimal solutions to the energy scheduling problem. Although the real-time optimization method eliminates the need to deal with the prediction uncertainties, it ignores the valuable statistical information used in day-ahead stochastic approaches and provides suboptimal solutions to the problem. The proposed adaptive approach combines the advantages of both the stochastic day-ahead and the RT-EMS and reduces the real-time operational cost of the microgrid. The proposed method moves the RT-EMS solution towards the optimal solution, by adding a penalty term to the objective function. Numerical results are provided to demonstrate the improved performance of the proposed adaptive method.

A two-stage IGDT/TPEM model for optimal operation of a smart building: A case study of Gheshm Island, Iran

Using poly-generation schemes based on energy hub (EH) concepts is one of the new ways to efficiently supply energy (electrical/thermal/cooling/water) demanded by smart buildings equipped with building energy management system (BEMS). Regarding severe uncertainties pertaining to renewable generation and demands, BEMS is faced with challenges to optimally operate EH during day-ahead. The present study proposes a stochastic day-ahead optimization model for BEMS to minimize the operation costs and emissions under a number of techno-economic constraints considering extreme uncertainties related to generations, demands, and prices. The proposed grid-connected EH includes combined cooling, heat, power (CCHP) system as dispatchable generation, and wind turbine (WT) and photovoltaic (PV) as non-dispatchable generations. To enhance the reliability of energy and water supply, two energy storage systems (ESSs), namely battery energy storage (BESS) and thermal energy storage systems (TESS) and one water storage tank (WST) have been installed in EH. Moreover, to improve flexibility of the proposed EH, it utilizes a seawater desalination (SWD) with reverse osmosis (RO) technology as an active load and an organic Rankine cycle (ORC) as a new technology for generating electrical power by means of thermal power. Also, it considers a demand response program as time of use (TOU) to enhance the flexibility of the operation. The proposed stochastic model is solved by a two-stage algorithm. The first stage formulates the uncertainties of the demands and renewable generations by two-point estimate method (TPEM) while the second stage models the extreme uncertainty of the gas price by information gap decision theory (IGDT) method as well as economically dispatching the generations and storages with risk-averse and risk-seeker strategies. The efficacy of the proposed model is evaluated on a smart commercial building in Gheshm located not far away from the southern coast of Iran in Persian Gulf. It is seen that the total cost of the EH equipped with a WST reduced by 16.55% compared to the one without a WST.

Stochastic day-ahead unit commitment scheduling of integrated electricity and gas networks with hydrogen energy storage (HES), plug-in electric vehicles (PEVs) and renewable energies

This study investigates stochastic day-ahead unit commitment scheduling of integrated natural gas and power systems that consist of a non-gas-fired unit (NGFU), gas-fired unit (GFU), combined heat and power (CHP) unit, and renewable production units considering AC power flow. Natural gas storage is incorporated into the problem to support the gas network during peak natural gas demand hours by reducing pipeline congestion. Moreover, integrating hydrogen energy storage (HES) along with new flexible technologies, such as power to gas (P2G) system and demand response program (DRP) is proven to reduce total operation cost by facilitating renewable energy dispatch and shifting peak load demand to off-peak hours. A sensitivity analysis is carried out to evaluate the impact of thermal load increment on the operation of the CHP unit as its thermal and electrical output are interrelated variables. A residential plug-in electric vehicle (PEV) charging station is integrated into the problem to observe its impact on the operation of the networks, as PEVs introduce a new unplanned and uncertain load to the system. Furthermore, the sensitivity of the problem for PEV penetration is investigated. The uncertain parameters of the problem are electrical load demand, PEVs’ load demand, and wind power. Eventually, the proposed mixed integer non-linear problem (MINLP) is applied to several case studies involving an integrated 6-bus power system and 6-node gas network.

Transactive energy management for optimal scheduling of interconnected microgrids with hydrogen energy storage

In recent years, renewable energy sources (RESs) have attracted substantial attention due to carbon-free and cost-effective advantages that have made them one of the main sources of energy generation in the modern structure of the power grid. This paper proposes the stochastic day-ahead scheduling model for optimal energy management of renewable-based microgrids. In this paper, each microgrid is equipped with 100% RESs including the PV system and wind turbine for full pollutant-free energy generation while the hydrogen energy storage (HES) system is used for alleviating the intermittences of the RESs aiming to dynamically balance the energy during a day. To model the fluctuations such as day-ahead market price in the microgrids, the autoregressive integrated moving average and fast forward selection methods are exerted for scenario production and reduction, respectively. Transactive energy as a sustainable and reliable technique is considered for controlling and coordinating energy sharing among the microgrids and the energy network for dynamic energy balancing in the deregulated environment. For energy management in the demand-side, the price and load response schemes are presented, aiming to revise the consumers' patterns in energy consumption in line with balancing energy and minimizing the microgrids’ energy cost. The effectiveness of the suggested model is validated using the modified IEEE 24-bus case study. The realistic modeling of the system based on the proposed model has led to an 8.51% increment in energy cost.

Stochastic reserve scheduling of energy storage system in energy and reserve markets

Energy storage systems (ESSs) can be used to participate in both the energy and reserve markets to maximize their reserve benefits. In contrast to traditional thermal units, ESSs have three statuses: charging status, discharging status, and idling status. The energy-limited feature of ESSs makes it difficult to schedule the reserve in the joint energy and reserve markets. In this paper, a detailed energy and reserve model of ESSs is considered in the joint stochastic day-ahead unit commitment model. The main highlights of this paper are the reserve model of ESSs. Compared to existing studies, a proposed six-mode hourly power reserve model of ESSs can schedule more reserve amount to participate in the reserve market. The six reserve modes include more charging/discharging, less charging/discharging, and switch from another status to charging/discharging. Besides, multi-hour coupled reserve constraints are introduced to avoid ESSs failures to provide the reserves scheduled in the day-ahead market. Considering the uncertainty of demand and renewable generation, a scenario-based stochastic model is adopted to calculate both the energy and reserve costs of the whole system. Two different systems are used to discuss the benefits of the proposed reserve model and analysis on some crucial parameters.

Stochastic Charging Optimization of V2G-Capable PEVs: A Comprehensive Model for Battery Aging and Customer Service Quality

This article proposes a new stochastic day-ahead residential charging model for a vehicle-to-grid (V2G)-capable plug-in electric vehicle (PEV). The aim is to minimize the expected customer’s charging cost, including energy cost and battery aging cost while satisfying the customer service quality constraints. The proposed model integrates a detailed PEV lithium-ion battery aging model as a function of average battery’s cell surface temperature, average current rate, average state of charge (SoC), and depth of discharge (DoD). Customer service quality constraints are mathematically modeled using Kano’s dissatisfaction model as an exponential function of the customer’s waiting time and charging level. Given the uncertain behavior of a PEV owner, the charging scheduling problem is formulated as a two-stage stochastic programming problem. In summary, this article contributes to the technical literature by developing a two-stage stochastic optimization framework for optimal charge scheduling of PEVs, which integrate a comprehensive battery aging cost model, and models customer dissatisfaction as Kano’s model-based function of the customer’s waiting time and charging level. Comparing the results in various deterministic, Monte Carlo simulation-based and the two-stage stochastic studies show that the proposed scheme can lead to low dissatisfaction for the customer, without a significant increment in costs.

Gaussian Mixture Based Uncertainty Modeling to Optimize Energy Management of Heterogeneous Building Neighborhoods: A Case Study of a Dutch University Medical Campus

To realize the goals of energy transition, becoming energy-neutral at the neighborhood level by sharing energy among clusters of heterogeneous buildings with local distributed energy resources (DERs), will play a vital role. However, uncertainties related to demand and renewable sources pose a major operational challenge to schedule the DERs. In this paper, a scenario-based mixed-integer linear programming (MILP) model is proposed for an energy management system (EMS) of a local energy community. The proposed EMS executes a stochastic day-ahead scheduling operation of multi-energy systems (MES). A set of scenarios are generated with the Gaussian mixture model (GMM) to consider uncertainties of demand and renewable sources. Moreover, Monte Carlo simulations (MCS) are performed to assess the effectiveness of the proposed EMS compared to the deterministic one. The proposed method is validated by using a real-world case study of a generic Dutch university medical campus in Amsterdam, the Netherlands. Two types of analysis are performed: one-day analysis and seasonal analysis. In both cases, in an average, the stochastic process outperforms the deterministic process considerably, in terms of cost, CO2 emission, imported electricity from grid and usage of local energy resources.

Assessment of wind power scenario creation methods for stochastic power systems operations

Probabilistic scenarios of renewable energy production, such as wind, have been gaining popularity for use in stochastic variants of power systems operations scheduling problems, allowing for optimal decision-making under uncertainty. The quality of the scenarios has a direct impact on the value of the resulting decisions, but until now, methods for creating scenarios have not been compared under realistic operational conditions. Here, we compare the quality of scenario sets created using three different methods, based on a simulated re-enactment of stochastic day-ahead unit commitment and subsequent dispatch for a realistic test system. We create scenarios using a dataset of forecasted and actual wind power values, scaled to evaluate the effects of increasing wind penetration levels. We show that the choice of scenario set can significantly impact system operating cost, renewable energy use, and the ability of the system to meet demand. This result has implications for the ability of system operators to efficiently integrate renewable production into their day-ahead planning, highlighting the need for the use of performance-based assessments for scenario evaluation.

Stochastic energy management in multi-carrier residential energy systems

This paper proposes a stochastic framework for residential energy management (REM) in a multi-carrier building. The proposed REM program minimizes the purchase cost of electricity and natural gas of a building, considering the operational constraints of energy dispatch and various building components such as micro-combined heat and power (CHP), heat storage system, heater, gas boiler, plug-in electric vehicles (PEVs), and renewable energy resources (RERs). A part of the electrical and thermal load of the building is assumed to be flexible in time and the amount of energy consumption. Uncertainties of renewable generation, the traveling time of PEVs, energy prices, and electricity/thermal loads are addressed in the program. Since the conventional stochastic methods are time-consuming or rely on asymptotic approximation, the proposed stochastic method in this paper has a low computational burden and a high precision level, which make it a suitable solution for large-scale stochastic programming problems. This method is examined on a test building for the stochastic day-ahead REM problem, and the results are compared with the other conventional methods to demonstrate the advantages of the proposed one.