Energy Market Prices Research Articles

Identifying optimal border of virtual power plants considering uncertainties and demand response

The virtual power plant (VPP) has the ability to control and manage the electrical energy flow between the aggregated units to obtain better operation of the system. This operation is aiming to minimize the cost of produced energy and therefore enhance the economical operation. To satisfy that, in the planning stage, it is important to select the optimal number of VPP buses, optimal location of distributed generations and optimal value and location of dispatchable load. In this paper, a novel methodology is introduced to identify the optimal border of VPP (location and number of VPP buses), identify the optimal number and location of distributed generations within VPP. In addition to identifying the optimal value and location of dispatchable load. Uncertainties related to VPP components are considered which include uncertain load demand, renewable energy resources and market prices. The simulation results are carried out on IEEE 57-bus test system and IEEE 39-bus system (new England-real system). Various capacities of distributed generations, different loading conditions and various maximum limits of VPP buses are investigated to assure the capability of the proposed methodology. Also, the performance of optimal VPP border under daily load is investigated. Added to all that, the capability of the proposed methodology is investigated under inflexibility of the network. Significant technical and economical benefits are achieved with the optimal VPPs bordering.

Demand side management for solving environment constrained economic dispatch of a microgrid system using hybrid MGWOSCACSA algorithm

Microgrids are a type of restricted power distribution systems in which electricity is generated, transmitted, and distributed within a small geographic region. They are used to ensure that renewable energy sources are used to their full potential. Microgrids provide further benefits, such as lowering transmission losses and the expenses associated with them. This research compares and contrasts the aims of economic dispatch, emission dispatch, fractional programing based combined economic emission dispatch, and environmental restricted economic dispatch (ECED). A low-voltage microgrid system is investigated for three different scenarios. As a study optimization tool, an innovative, resilient, and strong hybrid swarm-intelligence optimization algorithm is utilised, which is based on combining the properties of the traditional grey-wolf optimiser, sine-cosine algorithm, and crow search algorithm. The employment of a time-of-use energy market pricing approach instead of a fixed pricing plan resulted in a 15% decrease in generation costs throughout the course of the research. When ECED was assessed with a 15%–20% demand side management based restructured load demand model for the microgrid system, the generation costs were reduced even further.

Risk‐averse scheduling of an energy hub in the presence of correlated uncertain variables considering time of use and real‐time pricing‐based demand response programs

AbstractIn this paper, a risk‐based probabilistic short‐term scheduling of a smart energy hub (SEH) is presented considering the uncertain variables and the correlation between them. Neglecting the uncertainty of renewable energy sources (RESs), demands and market prices can make the obtained results unusable. In addition, correlations among uncertain variables may have similar importance on final solutions. To have a more realistic view, the stochastic nature of solar irradiation, wind generation, energy demands, and electrical/thermal/gas market prices are taken into consideration through uncertainty modeling. For this purpose, a probabilistic scenario‐based approach is implemented. The Monte Carlo simulation technique is employed to generate an adequate number of scenarios and the Cholesky decomposition technique combined with Nataf transformation is used to make the samples correlated. In addition, the k‐means data clustering technique is used to reduce the initial number of scenarios to the most representative 10 scenarios. The addressed SEH comprises photovoltaic panels/a wind turbine/a combined heat and power generation unit/a fuel‐cells power plant (FCPP)/a thermal/hydrogen storage system and plug‐in electric vehicles (PEVs). This study aims to optimize the economic aspects while reducing the pollution emissions of the SEH and controlling the risk level of SEH operation. To enhance the flexibility of the SEH in the management of supplying demands with lower costs, the thermal demand response program (DRP) is considered beside the electrical DRP. Two kinds of time of use (TOU) and real‐time pricing (RTP) DRPs are used for electrical and thermal loads. The conditional value at risk technique is taken into account to control the deviations of the SEH operation and emission costs. Simulation results show a reasonable reduction in operation and emission costs along with the risk level of the energy hub with the proposed approach. The operation emission, and risk costs are reduced by 37.39%, 32.11%, and 33.16%, respectively, with integrating PEVs, FCPP, and RTP‐DRPs. Moreover, integration of PEVs, FCPP along with TOU‐based DRPs contribute to reduce the operation emission, and risk costs by 10.47%, 9.03%, and 11.64%, respectively.

Optimal probabilistic operation of energy hub with various energy converters and electrical storage based on electricity, heat, natural gas, and biomass by proposing innovative uncertainty modeling methods

In recent years, attention to energy hubs (EHs) has increased significantly as active and intelligent elements in multi-energy systems (MESs). This article proposes a stochastic framework for the optimal operation of a new EH structure with various energy converters and electric storage based on electricity, heat, natural gas, and biomass. The proposed framework plays the role of a bidding strategy for a smart element in MESs. For modeling uncertainties in this framework, such as energy market prices, wind speed, and solar radiation, it is necessary to generate random scenarios based on recorded past behaviors or forecasting future trends. The Monte Carlo (MC) and the ARIMA methods have received significant attention in the literature to generate scenarios. Proposing uncertainty modeling methods in this paper, including the MC based on the Kolmogorov-Smirnov test, the ARIMA model based on Akaike Information Criterion, TBATS model, and the LSTM model of deep learning, as another innovation, has been such that efforts are made to make a significant improvement in the generated scenarios. Comparing various proposed uncertainty modeling methods is one of the most contributions. Based on the actual data in Finland, the simulation results demonstrate the effectiveness of the proposed operation strategy and the uncertainty modeling methods. Increasing the accuracy of uncertainty modeling has a significant impact on EH's profit and energy storage behavior and can also reduce the dependence of EHs on incoming energy carriers.

Competitive Energy Storage and the Duck Curve

Power systems with high penetrations of solar generation need to replace solar output when it falls rapidly in the late afternoon—the duck curve problem. Storage is a carbon-free solution to this problem. This essay considers investment in generation and storage to minimize expected cost in a Boiteux-Turvey-style model of an electric power system with alternating daytime time periods, with solar generation, and nighttime periods, without it. In the most interesting cases, if energy market prices are uncapped, all expected cost minima are long-run competitive equilibria, and the long-run equilibrium value of storage capacity minimizes expected system cost conditional on generation capacities.

Techno-Economic Analysis of Scenarios on Energy and Phosphorus Recovery from Mono- and Co-Combustion of Municipal Sewage Sludge

This study evaluates the techno-economic feasibility of energy and phosphorus (P) fertilizer (PF) recovery from municipal sewage sludge (MSS) through incineration in new combustion plants. We evaluated the economic impact of five critical process design choices: (1) boiler type, (2) fuel (MSS mono-combustion/co-combustion with wheat straw), (3) production scale (10/100 MW), (4) products (heat, electricity, PF), and (5) ash destination. Aspen Plus modeling provided mass and energy balances of each technology scenario. The economic feasibility was evaluated by calculating the minimum selling price of the products, as well as the MSS gate fees required to reach profitability. The dependency on key boundary conditions (operating time, market prices, policy support) was also evaluated. The results showed a significant dependency on both energy and fertilizer market prices and on financial support in the form of an MSS gate fee. Heat was preferred over combined heat and power (CHP), which was feasible only on the largest scale (100 MW) at maximum annual operating time (8000 h/y). Co-combustion showed lower heat recovery cost (19–30 €/MWh) than mono-combustion (29–66 €/MWh) due to 25–35% lower energy demand and 17–25% higher fuel heating value. Co-combustion also showed promising performance for P recovery, as PF could be recovered without ash post-treatment and sold at a competitive price, and co-combustion could be applicable also in smaller cities. When implementing ash post-treatment, the final cost of ash-based PF was more than four times the price of commercial PF. In conclusion, investment in a new combustion plant for MSS treatment appears conditional to gate fees unless the boundary conditions would change significantly.

Risk-adjusted preferences of utility companies and institutional investors for battery storage and green hydrogen investment

Achieving climate-neutrality requires considerable investment in energy storage systems (ESS) to integrate variable renewable energy sources into the grid. However, investments into ESS are often unprofitable, in particular for grid-scale battery storage and green hydrogen technologies, prompting many actors to call for policy intervention. This study investigates investor-specific risk-return preferences for ESS investment and derives policy recommendations. Insights are drawn from 1,605 experimental investment-related decisions obtained from 42 high-level institutional investors and utility representatives. Results reveal that both investor groups view revenue stacking as key to making ESS investment viable. While the expected return on investment is the most important project characteristic, risk-return preferences for other features diverge between groups. Institutional investors appear more open to exploring new technological ventures (20% of utility respondents would not consider making investments into solar photovoltaic-hydrogen), whereas utilities seem to prefer greenfield projects (23% of surveyed institutional investors rejected such projects). Interestingly, both groups show strong aversion towards energy market price risk. Institutional investors require a premium of 6.87 percentage points and utilities 5.54 percentage points for moving from a position of fully hedged against market price risk to a scenario where only 20% of revenue is fixed, underlining the need for policy support.

Modeling and Economic Operation of Energy Hub Considering Energy Market Price and Demand

This paper discusses the economic operation strategy of the energy hub, which is being established in South Korea. The energy hub has five energy conversion devices: a turbo expander generator, a normal fuel cell, a fuel cell with a hydrogen outlet, a small-scale combined heat and power device, and a photovoltaic device. We are developing the most economically beneficial operation strategy for the operators who own the hub, without making any systematic improvements to the energy market. First, sixteen conversion efficiency matrices can be achieved by turning each device (except the PV) on or off. Next, even the same energy must be divided into different energy flows according to price. The energy flow is controlled to obtain the maximum profit, considering the internal load of the energy hub and the price fluctuations of the energy market. Using our operating strategy, the return on investment period is approximately 9.9 years, which is three years shorter than that without the operating strategy.

Optimal Battery Energy Storage Dispatch in Energy and Frequency Regulation Markets While Peak Shaving an EV Fast Charging Station

Battery Energy Storage Systems typically procure their primary revenues from regulated energy and ancillary services markets; nonetheless, they have great potential in supporting distribution network operators and their users. This paper evaluates the potential business case of battery storage systems integrating market application and services to a photovoltaic assisted electric vehicle fast-charging station. A mathematical deterministic optimization problem is formulated using mixed-integer linear programming to combine battery storage system operation in the day-ahead and frequency regulation market and the remunerated services offered to the charging station. The technical and economic feasibility of the solution and the applicability of the proposed framework is verified through a case study reflecting an existing photovoltaic assisted charging station in the Netherlands and considering the Dutch energy market framework and prices. The study shows that such battery storage system implementation is economically and technically advantageous for the players involved. The battery storage system can stack additional revenues on top of the market revenues. The charging station benefits from a reduced peak power and a 30% tariff reduction, and the system operator would indirectly benefit from the shaved charging station profile. Furthermore, the analysis shows that providing services to the charging station from the battery storage system does not significantly impact its market-related revenues.

An experimental study of capacity remuneration mechanisms in the electricity industry

After electricity liberalization, the “energy-only market” design lacks effective incentives to invest in new capacity. Across the world, capacity remuneration mechanisms have been taking hold as an alternative to the energy-only market to ensure adequate power generation capacity. The way these designs are characterized reflects the insight from general economic theory on market power and strategic behavior. Indeed, Europe is heading toward a patchwork of different, uncoordinated national mechanisms. In practice, design choices are driven by national policies, needs and constraints. In addition, they are progressively converging toward a market-based mechanism with a forward period and market power mitigation. In this paper, we investigate their efficiency properties in terms of new investments, the reduction in unserved energy frequency and the energy prices of a generic capacity remuneration mechanism that is impervious to the so-called forward capacity market. The results from laboratory experiments confirm that this mechanism gives better incentives to invest in new capacity than the energy-only market alone and provides an empirical understanding of the investment decision process. The forward capacity market contributes to lower energy market prices in peak demand and extra-high peak demand periods and average energy costs if we consider the social cost of unserved energy.

Defining the Main Parameters and Performances of the Elevator Motor

Contemporary tendencies relentlessly dictate the conditions for the appearance of a more qualitative, reliable and comfortable elevator chain for the rolling stock of a vertical motion. At the same time, the issues of energy saving and cost-effective use of resources gain currency against the background of rising prices for energy carriers and market prices for various elements that play an essential role in the availability of many electromechanical systems. Unfortunately, attention was paid to the availability of above problems in the elevator sector when the majority of the elevators (about 60% of them) outlived their technical service life that ensured the reliable operation. As a matter of fact, an amazingly important issue is relating to the embedment of reliable, durable and economically substantiated components of electromechanical systems into contemporary Ukrainian elevators. The purpose of the research done was to define the main parameters and performances of the asynchronous elevator motor of an ADB180M6 type. The motor is powered from the industrial network of 50 Hz and the frequency converter with the frequency of 50Hz and 16.6 Hz. This scientific paper uses the methods of physical investigations. The elevator motor test data satisfy the reliability parameters that make any elevator user feel comfortable. The main measurement data obtained for the engine No43886 of an ADB180M6 type powered from the frequency converter “Altivar” of 22kW with the motor speed of 910 rpm and 289 rpm satisfy the requirements of the regulatory documentation. The noise level is within satisfactory margins.

Stochastic short-term scheduling of a wind-solar-hydro complementary system considering both the day-ahead market bidding and bilateral contracts decomposition

Complementary operation of hydropower, wind and photovoltaic (PV) power is one of the most cost-efficient solutions for integration of intermittent wind and PV power. In a deregulated market, synergetic optimization of day-ahead (DA) market bids and the decomposition of daily physical bilateral contracts (BCs) can increase profits for a wind-solar-hydro complementary system (WSHCS) by coordinating the spot market and the forward market. However, this short-term scheduling problem is challenging because of complex market mechanisms and uncertainties of multiple energy resources and market prices. A stochastic scheduling model is established to address this issue. First, uncertainties of wind, PV power and DA market prices are characterized by stochastic scenarios simulation with the adoption of the simultaneous backward reduction algorithm. Then, a two-layer nested optimization framework is conducted to optimize both the DA market bidding strategy and the decomposition of daily BCs. A WSHCS in China’s Yalong River Basin is selected as a case study. Results show that: the proposed model can decompose the total energy of the daily BC reasonably by leaving flexible bidding spaces in the DA market according to price variation, thus larger volume of bidding energy can be sold when DA market prices are higher than the contract price. Besides, the optimal decomposition outperforms the average decomposition of daily BCs with larger benefits, less power curtailment, and higher water resources utilization efficiency. These findings verify the effectiveness and applicability of the proposed model for synergetic optimization of both the decomposition of daily BCs and DA market bids for a WSHCS.

Research on bidding strategy of virtual power plant considering carbon-electricity integrated market mechanism

Distributed energy resources and controllable load can be aggregated in virtual power plant to participate in the bidding of day-ahead electricity market, intraday demand response market, regulation market, real-time electricity market and carbon trading market. However, the uncertainty output of distributed energy generation brings higher transaction risk to virtual power plant. Given this background, an operation model including wind turbine, electric vehicle, gas turbine and controllable load are constructed in virtual power plant. Through further analysis the carbon-electricity integrated market characteristics, a carbon-electricity integrated optimal bidding strategy of the virtual power plant is constructed, and the uncertainty of renewable energy power generation, load and market price is considered. Some assumptions are made, and a joint bidding strategy model for a VPP participating in bidding of the carbon-electricity integrated market is presented based on the robust optimization theory. Then, the commercial solver CPLEX 12.2 is next used to solve the developed robust optimization model. Finally, a virtual power plant demonstration area is used as the examples to demonstrate the feasibility and efficiency of the developed model and algorithm. The result shows that the electric vehicles and demand response transaction can effectively stabilize output fluctuation of renewable energy power generation, promote the utilization of renewable energy and improve the economic benefits of virtual power plant. Introducing carbon trading mechanism can significantly reduce the bidding output of high-carbon generation unit, and then affect the energy output structure and bidding strategy in virtual power plant.

Optimal Energy Management of a Campus Microgrid Considering Financial and Economic Analysis with Demand Response Strategies

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts.

Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy

In this paper, a risk-constrained stochastic mixed-integer linear programming (MILP) model is proposed for optimal bidding strategy of a grid-connected CHP-based multi-microgrid (MMG) system in energy and reserve markets considering environmental restrictions. A reward-based demand response program is considered in the proposed framework to realize demand-side management and boost the economic performance of the MMG system. Besides, conditional value-at-risk as a proper risk-averse index is incorporated into the developed mixed-integer programming formulation to hedge the risk of the low profits associated with worst scenarios. For this study, the uncertainty of energy market price, electrical load, and power generation of renewable energy sources are taken into account through employing stochastic programming. Meanwhile, given the global policy for emissions reduction, an emission constraint is considered into the model, ensuring a more green trading strategy. The obtained simulation results over a 24-h time horizon, demonstrate that increasing the risk factor leads to the profit decrement by almost 30.37% while the conservativeness of the MMG against the risk is improved. Also, the results reveal that by increasing the value of emission factor, the system's profit is decreased by about 30.03% while the MMG would be operated more environmental-friendly.

Correlation of Day Ahead Electric Energy Market Price with Renewable Energy Sources Generation and Load Forecast

Abstract An increase in the share of renewable energy sources has an impact on generation variability and generation forecast error, which is also reflected in the formation of the day ahead electric energy market price. This paper analyses the impact of variable renewable energy sources and load forecast on the day ahead German electric energy market. A correlation between the market price, renewable energy sources generation and load forecasts was analysed. Results show that there is a significant correlation between the forecast of renewable energy sources generation as well as the load on day ahead electric energy market price. However, a residual load, which represents the difference between the load and the renewable generation, has the biggest impact on the day ahead price.

Real-Time Bidding Model of Cryptocurrency Energy Trading Platform

Blockchain technology provides a comprehensive solution to user access and energy trading for distributed energy Internet. Achieving market-based pricing, increasing the earnings of energy suppliers, attracting foreign capital and facilitating the upgrade of solar and wind energy are pressing issues. Drawing on the practices of centralised exchanges and blockchain cryptocurrency, the author designed the Cryptocurrency Energy Trading Platform (CETP), dividing the permissioned blockchain into the Energy Blockchain Platform (EBP) and the Energy Cryptocurrency Exchange (ECE). The frequently used real-time bidding scenario and the seldom-used power-using scenario are separated from each other. A market welfare model for real-time bidding is established and verified. With Energy Blockchain Cryptocurrency (EBC) as the trading medium, the platform allows external bidders to get involved in the bidding and transactions, which not only attracts the social capital to be used in the development of energy Internet but also helps stabilise the energy market prices, thus, advancing the energy Internet.

Multi-objective optimization of energy-efficient production schedules using genetic algorithms

The optimization of production schedules to be more energy efficient while still meeting production goals is a difficult task: How to schedule and distribute production tasks to meet production goals, while making best use of fluctuating energy market prices and availability of locally installed energy sources? Although a large body of related work exists in this domain, most of those seem to focus on individual aspects and not the whole picture. In this paper, a genetic algorithm for optimization of production schedules with respect to energy consumption, peak shaving, and makespan is presented, that also takes into account that tasks can be performed in different ways, having different characteristics. The algorithm has been successfully employed within the SPEAR project by applying it for optimization of an automotive production line and for the pathway of an automated guided vehicle.

The Impact of Renewable Energy Generation on the Spot Market Price in Germany: Ex-Post Analysis using Boosting Method

This study combines regression analysis with machine learning analysis to study the merit order effect of renewable energy focusing on German market, the largest market in Europe with high renewable energy penetration. The results show that electricity from wind and solar sources reduced the spot market price by 9.64 €/MWh on average during the period from 2010 to 2017. Wind had a relatively stable impact across the day, ranging from 5.88 €/MWh to 8.04 €/MWh, while the solar energy impact varied greatly across different hours, ranging from 0.24 €/MWh to 11.78 €/MWh and having a stronger impact than wind during peak hours. The results also show characteristics of the interactions between renewable energy and spot market prices, including the slightly diminishing merit order effect of renewable energy at high generation volumes. Finally, a scenario-based analysis illustrates how different proportions of wind and solar energies affect the spot market price

An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy

Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units.