Market Price Uncertainty Research Articles

Systematic Categorization of Optimization Strategies for Virtual Power Plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

Portfolio management for a wind‐storage system based on distributionally robust optimisation considering a flexible ramping product

A wind-storage system (WSS) can use the synergy between wind farms and battery storages to mitigate the uncertainty of wind. With the flexible ramping capability of battery storages, a WSS can diversify its portfolio by selling energy, committing regulation services and offering flexible ramping products in a power market. The portfolio management problem of a WSS is studied here. The uncertainties of market prices and wind power are considered. In order to maximise the expected profit in the worst case, a distributionally robust optimisation model is proposed. The model is then reformulated as a solvable semidefinite optimisation problem. Real market data are used in the simulation, which validates the proposed method.

A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization

As renewable energy installation costs decrease and environmentally-friendly policies are progressively applied in many countries, distributed generation has emerged as the new archetype of energy generation and distribution. The design and economic feasibility of distributed generation systems is constrained by the operation of the microgrid, which has to consider the uncertainty of renewable energy sources, consumption habits and electricity market prices. In this paper, a mathematical model intended to optimize the design and economic feasibility of a microgrid is proposed. After a search in the state-of-the-art, weaknesses and strengths of existing models have been identified and taken into account for building the present model. The present model should be seen as a basis on which other models can be built upon, hence a complete definition of the different sub-models is stated: uncertainty modelling, optimization technique, physical constraints and regulatory framework. One of the main features presented is the generation of synthetic data in uncertainty modelling, employed to enhance the reliability of the model by taking into account a longer time horizon and a shorter time step. Results show significant details about energy management and prove the suitability of using a stochastic approach rather than deterministic or intuitive ones to perform the optimization.

Economic scheduling of a smart microgrid utilizing the benefits of plug-in electric vehicles contracts with a comprehensive model of information-gap decision theory

In this paper, a hybrid optimization approach is proposed to smart microgrid (SMG) operators to schedule the existing energy resources optimally to meet the load demand of the system. The proposed hybrid optimization is structured based on information-gap decision theory (IGDT) and two-stage stochastic programming to minimize the operational cost of the system in day-ahead (DA) and real-time (RT) power markets. The uncertainty of the day-ahead market price is tackled with the IGDT. The IGDT is furnished with robustness and opportunity functions leading to various risk-averse and risk-taker decision-making strategies. The problem is structured as bi-level programming, while by implementing the concept of envelop bounds, it is transferred into a single-level optimization problem. On the other hand, the uncertainties of the operation of the real-time market are modeled using scenarios with stochastic programming. The studied SMG is equipped with a photovoltaic system, a wind turbine, two microturbines (MTs), and battery storage. The results determine the scheduling of day-ahead exchange power and scheduled PEV contracts as here-and-now decisions of the optimization. However, the traded energy in the real-time market, MTs’ dispatch, and the charging/discharging plan of battery storage are wait-and-see decisions made by the second stage of the programming. The results are obtained and discussed from the perspective of risk-neutral, risk-averse, and risk-taker decision-makers. As an example of performance, when the uncertainty horizon is increased by 10% in the robustness mode, the operation cost is increased by about 27.5%. In comparison, the same amount of increment of uncertainty horizon leads to cost-saving about 33.3% addressing the positive aspect of uncertainty using opportunity function.

Coordinated scheduling of renewable energy balancing group

In the post incentives era, renewable energy sources (RES) need to become balancing responsible market participants. As their controllability is limited and questionably economically feasible, they will, mostly likely, join an existing balancing group or form a new one capable of flexible operation driven by market signals. In line with this, the paper proposes a novel concept of a new market balancing group, coordinating participation of a wind power plant and flexible sources presented by a single actor, the aggregator. The model is cast as a stochastic mixed integer linear programming (MILP) bilevel model where the upper level model is profit maximization of the new market balancing group, while the lower level problem models minimization of end user electricity cost. The two entities collaborate to reduce deviations from market schedules, where their mutual exchange occurs under zero cost. Individual and coordinated market participation are compared considering uncertainties of RES generation and market prices. The results show both cases; when coordinated participation creates financial benefits for both wind power plant and end-consumers, but also scenarios under which end-consumers will not consider offering their flexibility at the market through aggregators as they are better off not changing their supplier or tariff system. The latter case implies inadequate market incentives and products for universal inclusion of flexible end-users into active and price responsive system participation.

Dynamic transactive energy in multi-microgrid systems considering independence performance index: A multi-objective optimization framework

This paper proposes a leader multi-follower optimization for energy transactions in the multi-microgrid systems. The transactions of microgrids with the distribution network operator can be provided by energy internet. The distribution network operator can buy/sell energy from/to the microgrids. The upper-level of the optimization is modeled as a multi-objective optimization in which the profit of distribution network operator, independence performance, and energy not supplied of the system have been optimized using the lexicography approach. In the lexicography approach, two-steps for objectives are defined. In the first step, the profit of the distribution network operator is maximized, while the independence performance index and energy not supplied of system have been optimized in the second step. In the lower-level, microgrids are considered as followers to minimize their costs. The uncertainty of renewable generation and market prices are applied to the model using scenario-generation and scenario-reduction technique. The proposed non-linear bi-level model is transformed to the linear single-level optimization using the Karush-Kuhn-Tucker optimality conditions and strong duality theorem. The proposed model is tested on the standard case study and the results show the efficiency of the proposed model in terms of independence index, energy not supplied, and greenhouse gas emissions.

Energy Pricing and Sharing Strategy Based on Hybrid Stochastic Robust Game Approach for a Virtual Energy Station With Energy Cells

In this paper, a novel energy pricing and sharing strategy is proposed to solve the energy management and market bidding problems of a virtual energy station (VES) in multi-carrier energy systems. First, the energy cell-tissue-based group interaction framework and the multiagent-based communication and control architecture are established in the VES. Second, the general model of an energy cell is built, in which the integrated demand response process and the automatic generation control mode of a combined heat and power unit are designed. Third, according to the classification of energy cells, the aggregation rules of the VES without the participation of third-party agents are proposed, and the internal trading mechanism of the VES led by a higher energy cell is designed based on a Stackelberg game. Moreover, a three-stage hybrid stochastic robust optimization strategy is proposed to address uncertainties in market prices and renewable energy output. Finally, the optimization model is linearized and solved by duality theory, KKT conditions, and column-and-constraint generation (C&CG) algorithm. Simulation results prove the rationality and effectiveness of the framework and business model for the VES.

Risk-constrained stochastic optimal allocation of energy storage system in virtual power plants

Abstract This paper aims to develop a decision-making procedure for efficient placement and sizing of energy storage system (ESS) within virtual power plants (VPPs) premises under the uncertainty of market price. The main aim is to minimize the overall cost of VPP within a fixed time horizon planning. The understudy VPP consists of wind turbine, photovoltaic system, curtailable loads, ESS, and diesel generators in which the VPP trades electricity with the upstream grid. The proposed framework investigates both optimal power and optimal energy of ESS based on the available budget for investment. To hedge against the uncertainty of investment, an efficient risk management manner based on the concept of conditional value at risk is applied. Moreover, from the reliability point of view, two reliability indices: the loss of load expectation and energy expected not served, are evaluated under different levels of investment to assess the impact of ESS on VPP reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved by the well-known General Algebraic Modeling System commercial software package. The effectiveness of the proposed risk-based optimization approach is demonstrated through vigorous case studies and comprehensive cost-benefit analysis.

Risk management of a renewable-based compressed air energy storage system using downside risk constraints approach

Abstract The financial risks imposed from the uncertain parameters is a considerable issue in the optimization problem of renewable-based energy systems. Due to the various risks in renewable-based energy systems, a practical and simple risk measurement approach can be efficient in the risk-based strategy selection process. In this paper, the downside risk constraints (DRC) approach as a novel risk measurement approach is proposed to manage the imposed risks from the uncertain parameters over the stochastic problems. Therefore, various uncertainties, including solar irradiation, temperature, wind speed, electricity demand, and electricity market price uncertainties, are modeled using the DRC approach along with the stochastic programming. In addition, the compressed air energy storage (CAES) and demand response program (DRP) is implemented to manage the imposed risks. By using the proposed risk measurement method, the system operator can obtain a risk-strategy that is independent of scenarios. According to the obtained results, the expected cost of the stochastic problem is $ 6145.62, which by using the DRC approach, the system operator by paying the 3.4% ($ 6353.5) more cost can guarantee itself against the financial risks. The advantage of the proposed DRC approach is the conversion of the stochastic problem to a deterministic scenario-independent problem.

Optimal hierarchical energy management scheme for networked microgrids considering uncertainties, demand response, and adjustable power

In this study, a novel strategy for transactive energy in the networked microgrids (MGs) is presented. This strategy consists of three stages: local optimisation, global optimisation, and re-local optimisation. In the local optimisation, the daily cost of each MG is minimised by local energy management systems (EMSs). The results of the local optimisation are sent to the central energy management system (CEMS), and global optimisation is performed by CEMS. Finally, the primary scheduling of MGs is modified in re-local optimisation. The adjustable power concept is introduced to provide power exchange among MGs and external units. Various uncertainties, such as loads, renewable generations, and market prices are deliberated to the model. To do so, the worst condition of forecasted values of renewable generations and load consumptions are considered to assign a safety margin. Besides, the uncertainty of market prices is implemented by using the variance matrix based on forecasted and real values of market prices in D days in advance. To evaluate the efficiency of the proposed model, it is tested on a standard case study. The simulation results show that the cost of MGs in the grid-connected mode is improved effectively.

A novel hybrid two-stage framework for flexible bidding strategy of reconfigurable micro-grid in day-ahead and real-time markets

Microgrids are going to be used in future intelligent grids as a promising technology to enable widespread utilization of renewable energy sources in a highly efficient and reliable manner. It is known that reconfiguration of micro-grids, using tie-line and sectionalizing switches, can provide more operational flexibility. Additionally, coordinated scheduling of flexible loads and energy storage systems can play an important role in the optimal scheduling of micro-grids; thus lowering the costs. This paper proposes an optimal bidding strategy for a micro-grid in day-ahead and real-time markets, based on AC power flow model, considering the hourly reconfiguration of the micro-grid. Fuel cell-based hydrogen energy storage and multiple shiftable loads are considered in the proposed method according to the load’s activity schedule. A reconfigurable micro-grid incorporates energy production and consumption of its local components to trade power in both day-ahead and real-time markets in order to maximize its profit as a private entity. The bidding problem faces issues due to the high level of uncertainties, consisting of wind power generation and electric load as well as variations of market prices. A hybrid two-stage bi-level optimization model is proposed to manage such uncertainties so that wind power, load demand, and day-ahead market prices are handled through scenario-based stochastic programming, and an information gap decision theory is applied to model the uncertainty of real-time market prices under two strategies, namely risk-seeker and risk-averse. The numerical simulation results confirm the effectiveness of the proposed model.

Dust effect impact on PV in an aggregation with wind and thermal powers

Abstract This paper is about the dust effect impact on photovoltaic systems on the profit of an electricity market agent acting as an aggregator of photovoltaic power, wind power, thermal power, and an energy storage system. Energy storage ensures arbitrage and smoothing of the variability of photovoltaic power and wind power. The market agent intends to derive bids for submission in a day-ahead market, having consideration of the dust effect impact on the photovoltaic power. A formulation is proposed for a support decision system by a profit-based unit commitment problem solved by a stochastic programming approach, considering the operating characteristics of the virtual power plant. The photovoltaic power, wind power, and market price uncertainties are input data derived from scenarios of historical data. Case studies addressed show the advantages of the stochastic programming approach and insights concerned with the integration of uncertainties within the modeling for the schedule of the energy storage system and the dust effect impact on profit.

RETRACTED: Optimal operation of storage-based hybrid energy system considering market price uncertainty and peak demand management

Abstract In order to stabilize the operation of power systems, it's essential to model the uncertainties threatening the normal performance of these systems. In this study, a renewable based grid-photovoltaic (PV)-boiler-battery-fuel cell hybrid energy system (RBHES) has been scheduled according to the uncertainty modeling of upstream net price and employment of demand response program (DRP). The main reason of implementing DRP is to motivate electricity consumers to modify their energy usage pattern in a way that economic goals of RBHES are obtained. In this paper, interval optimization technique has been used to model the uncertainty of upstream net price and to prepare a stabilized condition for safe operation of RBHES. Converting the single-objective-uncertainty-based model into a deterministic multi-objective model with average and deviation costs, the interval optimization technique models uncertainty and guarantees the optimal performance of RBHES under the minimum impact of upstream net price uncertainty. To solve the aforementioned multi-objective model, the weighted sum technique and the fuzzy approach are used. An RBHES has been investigated as a case study and the simulation results showing positive effects of employed techniques are presented for comparison. In cases with and without DRP, the average costs of RBHES in the interval method in comparison with the deterministic method have increased 1.61 % and 2.06 %, respectively. This is while the deviation costs of RBHES has reduced up to 13.61 % and 15.28 %, respectively. Due to the successful implementation of the DRP, the average cost and the deviation cost of the RBHES have lessened for 5.89 % and 11.08 %, respectively, in comparison with the case lacking the DRP.

Stochastic simulation of Brazilian forward energy prices

Energy trading offers great gain opportunities but also exposure to risk due to price volatility. A proper price model is a key element of risk analysis by electricity market traders and represents a strong tool for providing a better understanding of market dynamics. Despite the existing methodologies, the agenda for new research development is still open, mainly considering specific regulatory conditions and the dynamics of energy markets under development. This work provides a technique to simulate stochastic forward price curves for the Brazilian energy market by using stochastic procedures widely used in the financial and stock markets. The results of this work conveniently represent the uncertainty in Brazilian energy market prices and can be applied in risk management and decision making.

Stochastic Optimal Energy Management and Pricing for Load Serving Entity With Aggregated TCLs of Smart Buildings: A Stackelberg Game Approach

With the development of demand-side management in the smart grid, load-serving entity (LSE) plays a more important role for consumers, which purchases energy from the electricity market and sells it to consumers. Moreover, aggregated thermostatically controlled loads (TCLs) in smart buildings can provide additional demand response capacities and require efficient energy management methods. This article proposes a stochastic optimal energy management and pricing model for LSE with aggregated TCLs and energy storage based on the Stackelberg game and stochastic programming. The energy management and pricing problem are formulated as a bilevel optimization model. The upper level model aims to maximize LSE's expected profit under market price uncertainties and determines the offering prices to consumers. According to the offering price from upper level model, the lower level model optimizes the power purchasing pattern for consumers of two types of buildings with TCLs: factory and office buildings. The nonlinear bilevel model is reformulated and converted into mixed-integer linear programming using a strong duality theory. The proposed model is validated by numerical studies based on real market prices from the PJM electricity market. In addition, the impacts of energy storage, number of buildings, comfortable indoor temperature limits, and offering price limits on LSE's profit are analyzed.

Bidding strategy of hybrid power plant in day‐ahead market as price maker through robust optimization

Renewable energy such as wind power plays an important role in power generation. Large amount of renewable energy can allow these producers to change the market price and be as a price maker in the market. Thus, we propose an optimal bidding strategy in the day-ahead market for the hybrid power plant that participates as a price maker in the day-ahead market and as a price taker in the balancing market. Hybrid power plant is composed of wind power producer, battery storage and demand response. The uncertainty of day-ahead market price is modeled through Robust Optimization. In case studies, bidding strategy of hybrid power plant as a price maker and price taker configuration is presented. Then, joint operation of hybrid power plant is compared to disjoint operation and the effect of participating in market as coordinated unit on total profit is investigated. Moreover, the changes in profit according to risk level are determined and finally, offering curves are obtained by dividing the confidence bounds of day-ahead market price to some subintervals. The simulation is done based on Nord Pool Market and the results validate hybrid power plant can behave as a price maker in the day-ahead market and the profit of all units increases in joint operation.

Techno-Economic Analysis of CO2 Capture Technologies in Offshore Natural Gas Field: Implications to Carbon Capture and Storage in Malaysia

Growing concern on global warming directly related to CO2 emissions is steering the implementation of carbon capture and storage (CCS). With Malaysia having an estimated 37 Tscfd (Trillion standard cubic feet) of natural gas remains undeveloped in CO2 containing natural gas fields, there is a need to assess the viability of CCS implementation. This study performs a techno-economic analysis for CCS at an offshore natural gas field in Malaysia. The framework includes a gas field model, revenue model, and cost model. A techno-economic spreadsheet consisting of Net Present Value (NPV), Payback Period (PBP), and Internal Rate of Return (IRR) is developed over the gas field’s production life of 15 years for four distinctive CO2 capture technologies, which are membrane, chemical absorption, physical absorption, and cryogenics. Results predict that physical absorption solvent (Selexol) as CO2 capture technology is most feasible with IRR of 15% and PBP of 7.94 years. The output from the techno-economic model and associated risks of the CCS project are quantified by employing sensitivity analysis (SA), which indicated that the project NPV is exceptionally sensitive to gas price. On this basis, the economic performance of the project is reliant on revenues from gas sales, which is dictated by gas market price uncertainties.

Risk and profit-based bidding and offering strategies for pumped hydro storage in the energy market

Pumped hydro storages (PHS) are the most common storage in the power system, which covers 99% of the total installed capacity of energy storage facilities in the world. Therefore, optimal offering and bidding strategies of PHS are essential in the energy market. Besides, various uncertainties, especially market price uncertainty is more challenging for PHS operator due to the imposed risks. Therefore, in order to optimally market-participation of these storages, in this paper a novel risk management approach called downside risk constraints (DRC) is applied to get the optimal offering and bidding strategies of the PHS. The DRC method is used along with the stochastic programming to model the risk associated with uncertainty parameter, including electricity market price uncertainty. Hence, ten scenarios are produced using the normal distribution function to model the uncertain parameter. Illustrated examples are determined how to deal with imposed risk from uncertainty by managing financial profit. Finally, according to the obtained results, using the proposed downside risk constraints, PHS operators can experience zero-risk conditions by decreasing their profit by 33% while risk-in-profit is reduced by 100% in versus risk-neutral strategy.

Optimal sizing of supporting facilities for a wind farm considering natural gas and electricity networks and markets constraints

Wind farms (WFs) have already been subjected to a considerable challenge to participate in competitive markets due to their power output uncertainty. Gas turbines (GTs) and energy storages could be an effective support for wind energy integration because of their fast and reliable response through providing the required flexibility in generating and storing electrical energy (EE). In this paper, optimal size of GTs, EE storage systems (ESS), and natural gas storage (GS) are addressed in adjacent a large WF in a generation company (GenCo) in order to maximize the GenCo’s profit in EE markets and to hedge its risks in either natural gas (NG) and EE markets. The proposed framework maintains a self-scheduling strategy for coordinated operation of GT, ESS, NGS, and the WF in different weather regimes as a stochastic mixed integer linear programing (MILP). Technical constraints of power system transmission and natural gas pipeline are considered, as well as the uncertainties of wind power generation, NG and electricity market prices, and also NG pipeline deliverability. The efficiency of the proposed method is benchmarked in different case studies.

A Two-Stage Optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets

Aimed at the coordination control problem of each unit caused by microgrid participation in the spot market and considering the randomness of wind and solar output and the uncertainty of spot market prices, a day-ahead real-time two-stage optimal scheduling model for microgrid was established by using the chance-constrained programming theory. On this basis, an improved particle swarm optimization (PSO) algorithm based on stochastic simulation technology was used to solve the problem and the effect of demand side management and confidence level on scheduling results is discussed. The example results verified the correctness and effectiveness of the proposed model, which can provide a theoretical basis in terms of reasonably coordinating the output of each unit in the microgrid in the spot market.