Energy Procurement Cost Research Articles

A Peer-to-Peer Energy Trading Framework for Wind Power Producers With Load Serving Entities in Retailing Layer

This article proposes a peer-to-peer (P2P) energy trading framework for wind power producer (WPP) in the retailing layer to increase its revenue and to promote wind power utilization. In this framework, the WPP can provide energy consumption of demand response providers (DRPs) in a competitive environment through both the main grid and rival load-serving entities. Also, based on a P2P pricing mechanism, the WPP can choose if to trade energy in a P2P platform or not. The proposed problem is formulated as a stochastic bilevel optimization model, in which in the upper level, the WPP profit is maximized and in the lower level, the DRPs participate in DR programs and tend to choose the fairest supplier among the WPP and LSEs to minimize their energy procurement costs. The proposed method is applied to a realistic case study and the results demonstrate that with P2P, the WPP can schedule the energy transaction with peers to offset part of the energy deviations. Moreover, different values of P2P prices lead to different values of energy transactions due to the relatively diversity pattern of local generation of rival LSEs and their offering prices and demand and generation of the WPP.

A rolling horizon approach for real-time trading and portfolio optimization of end-user flexibilities

Flexible energy consumption patterns represent a significant potential for end-users to reduce their energy costs due to the increasing price volatility in the electricity spot markets. However, a simple and exhaustive description of flexibilities is needed to efficiently coordinate multiple flexible components. In this paper, we propose a simple and complete method to describe the end-user flexibility potential. The proposed method describes flexibilities of the end-user technologies as a combination of inflexible loads and virtual batteries with variable capacities. This technique allows to describe the flexible components (such as heat pumps and boilers) with the exclusive use of linear relationships which can be implemented in mixed integer optimization problems. Furthermore, a rolling horizon optimization framework is used to define the operating strategy of the end-user components and the trades at the Day-Ahead and the Intraday spot markets. Moreover, we apply our proposed methods to a real-life use case in Austria with measured data to prove their effectiveness, validity and reliability. The results show that trading energy in the Day-Ahead and the Intraday spot markets can lead the end-user to a reduction of the energy procurement costs of 8%, but at the same time increases its energy consumption by 3.21%.

Real-Time Load Scheduling, Energy Storage Control and Comfort Management for Grid-Connected Solar Integrated Smart Buildings

Energy storage control, load scheduling, and indoor user comfort management are perceived as key management solutions for electric industry in the building sector. Nevertheless, requirement of a-priori knowledge on system inputs (i.e., renewable energy generation process, load arrival process, and dynamic price signals) raises concerns about the ability of existing building energy management solutions to accurately adapt to real-time needs in energy generation, demand, storage, and indoor comfort feel. Conversely, with the consideration of unknown dynamics of system inputs, a one-slot-look-ahead virtual queue stability based Lyapunov optimization technique is employed in this article for a real-time energy and comfort optimization in grid-connected solar integrated smart buildings. The goal is to minimize an average aggregated system cost through a real-time joint optimization of electrical and thermal load scheduling delays, energy procurement cost from controllable generators and external grid, electrical and thermal energy storage degradation, and indoor user comfort feel. It is also shown that the joint optimization problem is separable into subproblems which are sequentially solved to obtain all solutions in closed-forms. The solutions are proved as asymptotically optimal, and can be easily implemented in real-time building energy and comfort management scenarios especially when the statistics of system inputs are unknown and arbitrary. The proposed algorithm is validated through simulations where it is tested in different weather conditions. Results show that the proposed algorithm can achieve an average monthly energy procurement-and-operations cost reduction up to 16.37%, while meeting building’s energy and comfort requirements.

Stochastic risk‐based flexibility scheduling for large customers with onsite solar generation

This study proposes a two-stage stochastic optimisation model to co-optimise the onsite flexibility resources of large customers with the decisions to purchase power from bilateral contracts and forward energy market, while considering the uncertainty of onsite solar generation and market prices. In addition to onsite solar generation, the proposed model integrates the energy flexibility provided by the customers’ flexible loads, onsite energy storage and onsite dispatchable generation. The uncertainty of onsite solar generation and market prices are characterised by autoregressive integrated moving average models, which are used to generate scenarios for the proposed stochastic optimisation model. The proposed two-stage stochastic optimisation model, formulated as a mixed-integer linear programming problem, minimises the expected energy procurement cost of the customers by optimising the first and second stage decisions. The first stage decisions include the utilisation of bilateral contracts, flexible loads and onsite energy storage and dispatchable generation, while decisions to purchase power from, and selling excess solar power to the energy market are optimised in the second stage. The proposed model integrates conditional value-at-risk as a risk measure that would enable the customers to manage the financial risks associated with the uncertainty of onsite solar generation and market prices. The proposed model is implemented on a test large industrial customer, where the advantages of the proposed model are investigated through multiple case studies.

Pricing Mechanism for Flexible Loads Using Distribution Grid Hedging Rights

This paper proposes a new pricing mechanism for the integration of flexible loads in distribution grids. This price is calculated from an envisioned two-layer local distribution grid market, where flexible load aggregators are price takers and the distribution system operator (DSO) is the market operator. On a day-ahead basis, the DSO runs the local market and clears distribution locational marginal prices (DLMPs). Aggregators purchase energy based on these DLMPs. However, depending upon grid conditions (losses/congestion), the DLMP value increases, causing aggregators to bear high energy procurement cost. To mitigate this issue, a second layer is introduced to compute ex-ante scenario-based Hedging Rights. Through a combination of these two layers, the proposed mechanism 1) improves market competition among the involved entities, 2) maintain the congestion alleviation property of DLMPs, and 3) incorporate the intertemporal energy requirements of flexible loads. The proposed framework is tested on an IEEE benchmark distribution grid. The simulation results show that the proposed pricing mechanism allows aggregators to achieve higher cost-savings, while preserving physical power flow evaluation feature of DLMP.

Optimal Market Participation of Distributed Load Resources Under Distribution Network Operational Limits and Renewable Generation Uncertainties

Distributed load resources are encouraged nowadays to actively participate in the energy market. As a part of the distribution system, they affect the power flow pattern of the network and interact with intermittent renewable generation in the distribution system. In this regard, one fundamental challenge, not yet addressed, is to derive an optimal market participation model, under the demand bidding paradigm, that systemically accounts for the operational limits of a physical distribution grid considering uncertainty associated with both the electricity market , and distribution network system . Accordingly, this paper addresses the optimal demand biding under uncertain market and distribution system data and network operational limits. Assuming a price-taker distribution utility with renewable energy, inflexible and deferrable loads and a two-settlement market model, we develop a two-stage robust stochastic bidding formulation solved using a decomposition algorithm. We derive optimal bid curves that minimize energy procurement cost and fully comply with the operational standards of the distribution network. Moreover, novel indexes are proposed to help the utility evaluate the operational performance of its network with regard to deferrable loads and renewable resources. Finally, we illustrate the advantage of the proposed model from a set of numerical experiments on an example system and the 33-bus system.

Roof-Top Stand-Alone PV Micro-Grid: A Joint Real-Time BES Management, Load Scheduling and Energy Procurement From a Peaker Generator

In this paper, we propose a user centric real-time (RT) technique for an optimal control of a distributed photovoltaic stand-alone micro-grid. Our objective is to minimize an average aggregated system cost over a finite time period considering a joint optimization problem of battery energy storage (BES) management, load scheduling, and energy procurement process from a peaker generator (PG). Due to online decision making, the dynamic control actions of the state of energy of the storage battery are correlated over time. Thus, we impose a constraint on the BES state of energy over the considered time period and eliminate the finite BES capacity constraint. In the load scheduling process, we account for the temporal variability and spatial uncertainty of each household load explicitly. We introduce the concept of block duration to optimize the energy procurement cost from a PG. We modify and then transform the problem to utilize the Lyapunov optimization technique. We show that the proposed solution to the joint optimization problem is asymptotically optimal with a bounded performance guarantee and is easy to implement. Simulations in different weather conditions show the effectiveness of our proposed user centric RT solution in terms of the selected performance metrics.

Optimal Conductor Size Selection in Distribution Networks with High Penetration of Distributed Generation Using Adaptive Genetic Algorithm

The widespread deployment of distributed generation (DG) has significantly impacted the planning and operation of current distribution networks. The environmental benefits and the reduced installation cost have been the primary drivers for the investment in large-scale wind farms and photovoltaics (PVs). However, the distribution network operators (DNOs) face the challenge of conductor upgrade and selection problems due to the increasing capacity of DG. In this paper, a hybrid optimization approach is introduced to solve the optimal conductor size selection (CSS) problem in the distribution network with high penetration of DGs. An adaptive genetic algorithm (AGA) is employed as the primary optimization strategy to find the optimal conductor sizes for distribution networks. The aim of the proposed approach is to minimize the sum of life-cycle cost (LCC) of the selected conductor and the total energy procurement cost during the expected operation periods. Alternating current optimal power flow (AC-OPF) analysis is applied as the secondary optimization strategy to capture the economic dispatch (ED) and return the results to the primary optimization process when a certain conductor arrangement is assigned by AGA. The effectiveness of the proposed algorithm for optimal CSS is validated through simulations on modified IEEE 33-bus and IEEE 69-bus distribution systems.

Cooling demand uncertainty modeling in solar photovoltaic based multi-chiller systems

In this paper, optimal scheduling of solar photovoltaic based multi-chiller systems is obtained in the presence of cooling demand uncertainty which a robust optimization approach is proposed for uncertainty modeling. Also, cooling demand response program is proposed which shift demand from peak periods to off-peak periods in order to flat cooling demand. Objective function is minimizing of energy procurement cost of multi-chiller systems from upstream grid in the presence of cooling demand response and uncertainty. The proposed model is formulated via MINLP which is solved using SBB solver under GAMS software. The effects of cooling demand response program are studied in the robust and deterministic strategies. Obtained results show that the risk-averse strategy via robust optimization approach is resistance versus cooling demand uncertainty in comparison with risk-neutral strategy via deterministic method. Also, cooling demand response program leads to reduce energy procurement cost in both deterministic method and robust approach.

Aggregation and Disaggregation of Energetic Flexibility From Distributed Energy Resources

A variety of energy resources has been identified as being flexible in their electric energy consumption or generation. This energetic flexibility can be used for various purposes such as minimizing energy procurement costs or providing ancillary services to power grids. To fully leverage the flexibility available from distributed small-scale resources, their flexibility must be quantified and aggregated. This paper introduces a generic and scalable approach for flexible energy systems to quantitatively describe and price their flexibility based on zonotopic sets. The description allows aggregators to efficiently aggregate the flexibility of large numbers of systems and to make control and market decisions on the aggregate level. In addition, an algorithm is presented that distributes aggregate-level control decisions among the individual systems of the population in an economically fair and computationally efficient way. The algorithm is applied to the problem of disaggregating reference schedules resulting from day-ahead energy markets.

Novel Incentive Mechanism for End-Users Enrolled in DLC-Based Demand Response Programs Within Stochastic Planning Context

In this paper, a novel direct load control (DLC) planning based on providing free energy credits to residential end-users for their heating, ventilation, and air conditioning load during demand response (DR) events is proposed. The obtained credit can then be used by the end-users during relatively higher price periods free-of-cost to enable them lowering their energy procurement costs. Furthermore, the resulting reduction in the total household energy consumption considerably decreases the critical load demands in power systems, which is of vital importance for load-serving entities in maintaining the balance between supply and demand during peak load periods. In this regard, the aforementioned energy credits-based incentive mechanism is proposed for end-users enrolled in the DLC-based DR program, as a new contribution to the existing literature, testing it in a stochastic day-ahead planning context.

Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory

Considering increasing distributed energy resources and responsive loads in smart grid paradigm, this study proposes a new approach for robust hourly energy scheduling of distribution systems at the presence of severe uncertain renewable energy sources (RES). Wind and photovoltaic power generations are considered as the RESs. The aim is to minimise the total energy procurement cost, while considering the participation of RESs, by their optimal allocation in the network. The inherent uncertainty of RESs is handled via information gap decision theory. One of the features of the proposed model is to consider the impact of demand response and energy storage system as the effective tools to reduce unintended costs due to uncertainty of RESs. Also, the proposed model handles the uncertainty of multiple RESs in a way that maximum tolerable uncertainty of RESs is achieved for a given worsening of total energy procurement cost. The proposed model is formulated as a mixed integer nonlinear optimisation problem and is implemented in general algebraic modelling system environment. The model is applied on the IEEE standard 33-bus radial test system, and the obtained results substantiate that the utilisation of ESS and DR can reduce the impact of RESs' uncertainty on the energy cost.

Integrated Demand Response Mechanism for Industrial Energy System Based on Multi-Energy Interaction

With the development of energy integration technologies, the conventional scope of demand response has been extended to the integrated demand response (IDR), which imparts the flexibility to consumers' energy demand. This paper explores interaction patterns for multi-energy demand management and proposes an IDR mechanism for the industrial integrated energy system. The mechanism exploits three interaction patterns to promote the interaction between the demand and supply of multiple energy systems. The incentive payment is provided not only for curtailing interruptible electric load, but also for adjusting flexible heating and cooling loads. The coupling characteristics of industrial consumers' multi-energy demands are then modeled to reflect the mutual influence between the consumption behaviors for different energy sources. Besides, based on the coupling of CCHP (Combined Cooling, Heating, and Power) energy outputs, the demand-supply interaction model is also proposed, which could change the electric generation by affecting consumers' heating and cooling demands. The optimization model of the IDR is then established with the objective of minimizing the total dispatch cost. The simulation results show that compared with conventional DR programs, the total dispatch cost and consumers' energy procurement cost are both reduced. The CCHP could also benefit from this mechanism. Furthermore, the impact of the coupling characteristics and the incentive price on the dispatch cost is also analyzed.

The chiller’s electricity consumption simulation by considering the demand response program in power system

One of the main challenges in multi-chill system issues by considering the uncertainty of cooling demand is total energy consumed and energy buying cost. Therefore, it is necessary to optimize power consumption in these issues. This paper provides a robust optimization method with uncertainty modeling in multi-chiller systems to obtain accurate planning. It should be noted that one of the energy supply sources for multi chiller system is photovoltaic system (in addition to the network). In order to improve cooling demand profile, using demand response program, demand levels are shifted through peak periods of energy consumption to low consumption periods. In this work, the minimization of energy procurement cost of multi-chiller systems from the upstream network is assumed as a target function, taking into account demand response of cooling and uncertainty demand. Also, the effect of cooling demand response on two robust and deterministic strategies has been investigated. Achieved results depict, risk-averse method through robust method is robust against cooling requirement uncertainty, contrasting to risk-neutral method through deterministic approach. In addition, the cost of supplying energy of multi-chiller systems from solar systems is reduced by using the demand response program in both of mentioned methods.

Optimal power consumption for demand response of thermostatically controlled loads

SummaryWe consider the problem of determining the optimal aggregate power consumption of a population of thermostatically controlled loads such as air conditioners. This is motivated by the need to synthesize the demand response for a load serving entity (LSE) catering a population of such customers. We show how the LSE can opportunistically design the aggregate reference consumption to minimize its energy procurement cost, given day‐ahead price, load forecast, and ambient temperature forecast, while respecting each individual load's comfort range constraints. The resulting synthesis problem is intractable when posed as a direct optimization problem after Euler discretization of the dynamics, since it results in a mixed‐integer linear programming problem with number of variables typically of the order of millions. In contrast, in this paper, we show that the problem is amenable to continuous‐time optimal control techniques. Numerical simulations elucidate how the LSE can use the optimal aggregate power consumption trajectory thus computed, for the purpose of demand response.

Uncertainty-based electricity procurement by retailer using robust optimization approach in the presence of demand response exchange

In this paper, robust optimization approach is proposed to handle market price uncertainty in which the upper deviation from forecasted value of pool price will be considered for risk analysis for a retailer. Objective of this paper is minimization of energy procurement cost for retailer from pool market, forward contracts and demand response programs (DRP). Therefore, three new designs of demand response (DR) programs have been proposed in this study which retailer can use to procure their required energy. These new DR schemes consist of pool-order option, forward-DR and reward-based DR contracts. The robust optimization approach examines the retailer’s performance at risk-averse and risk-neutral strategies, in which risk-neutral explains the normal performance of retailer, and risk-averse explains the risky performance of retailer. The proposed robust scheduling of retailer is modeled via MIP model which can be solved using CPLEX solver under GAMS software. The achieved results show that the retailer cost in risk-neutral strategy is reduced due to use of new DR schemes. Also, in risk-averse strategy, retailer cost reduction is more than the risk-neutral strategy use of new DR schemes.

A retrospective analysis of the market price response to distributed photovoltaic generation in California

Due to rapid growth of distributed solar photovoltaic (PV) capacity in the U.S., numerous “value of solar” studies have attempted to quantify avoided costs associated with distributed PV. One such avoided cost that has received little attention is the market price response, or how distributed PV generation reduces utilities’ procurement costs and, consequently, consumers’ costs through reduced wholesale electricity prices in the short-term. We quantify the reduction in day-ahead wholesale electricity prices to distributed PV generation in California (CA) from 2013 through 2015. Using a database of all distributed PV systems in the three CA investor owned utilities, we estimate historic hourly distributed PV generation using three methods that we validate with metered generation from 205 PV systems. Via multiple linear regression, we then estimate electricity price reductions due to distributed PV generation. Across the three methods used to estimate PV generation, distributed PV generation reduced hourly median (mean) wholesale electricity prices by up to $2.7–3.1/MWh ($2.9–3.2/MWh) ($2015), or by 7–8% (8–9%). Lower wholesale prices reduced utilities’ energy procurement costs in the day-ahead market by up to $650–730 million ($2015) from 2013 through 2015. These avoided costs are similar to other avoided costs commonly included in value of solar studies.

Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management

The operator of renewable-based microgrid (MG) tries to supply the local load with the lowest cost from the alternative energy sources containing upstream grid, micro-turbines (MTs), renewable energy sources (RESs) (photovoltaic (PV) systems and wind-turbines (WT)) and energy storage system (ESS). To purchase power from upstream grid, the optimal bidding curve of MG should be prepared to bid the market operator. Therefore, this paper proposes an information gap decision theory (IGDT) to obtain the bidding strategy of MG. IGDT includes the robustness and opportunity functions for upstream grid price uncertainty modeling. MG can consider the robustness decision (risk-averse) or the opportunity decision (risk-taking) under uncertainty environment. Also, the operator of MG uses the demand response program (DRP) which purpose is to reduce the energy procurement cost. Meanwhile, the proposed stochastic model considers the uncertainty modeling of local load and RESs output power by using a scenario stochastic model. To show the capability of proposed approach, two cases considering without and with DRP are studied. Beneficial results of DRP are utilized in case B, which the operation cost in case B is% 4.6 less than case A.

Design and performance investigation of a novel absorption ice-making system using waste heat recovery from flue gases of air to air heat pump

Nowadays, different renewable sources and energy storages are attracting world attention due to the major concerns about the excessive emissions and global energy crisis. Hence, this paper studies design and optimal short-term evaluating of a green absorption ice-making system, which comprises of an air to air heat pump (AAHP) and a Stirling engine. Energy-exergy analysis is used for model investigation. In the proposed system, the AAHP is equipped with an inside evaporator fan, an outside condenser fan, an expansion valve, compressor, condenser and an evaporator to supply total cooling demand of large residential buildings during the extremely hot summer days or transient heat waves. In the presented model, the delicate ice crystals can be formed through the AAHP’s refrigeration cycle by blowing the chilled inside air at the lowest temperature of −8.6 °C to a water tank. Expected energy requirement of compressor, evaporator and condenser fans can be supplied either by Stirling engine or by local electricity grid. Meanwhile, a mid-high temperature waste heat is recovered from the exhaust air of the condenser fan in order to drive the Stirling engine for the next hour. A comprehensive analysis is developed as a non-linear programming (NLP) problem to minimize total energy procurement cost over a 24-h time interval taking into account the operational constraints of AAHP and Stirling engine. Simulation results reveal the validity and effectiveness of the proposed ice-harvesting system with zero-CO2 emissions and near-zero energy consumption.

Design and robust optimization of a novel industrial continuous heat treatment furnace

This paper designs and robustly schedules a novel AAHP (air to air heat pump) based ICHTF (industrial continuous heat treatment furnace) which is driven by a solar Stirling engine and a wind turbine. The proposed flame-making process equipped with two inside and outside air fans, an expansion valve, compressor, condenser, and an evaporator to supply the heating demand with the high-temperature more than 1000 K.The heating energy obtained from the AAHP is not produced during the combustion reactions but is transferred from the ambient air to the furnace chamber. Therefore, use of solar, wind and AAHP in an industrial furnace with no need to fossil fuels not only reduces the stack pollutant emissions and ozone-depleting substances, but also increases the economic savings in the consumptions of petroleum products and electricity, significantly. In this study, an energy-exergy based mathematical framework is comprehensively presented to model the proposed ICHTF in the presence of renewable energy sources such as air, solar, and wind. Moreover, an interval robust economic optimization strategy is introduced to minimize total energy procurement cost using the forecasted wind speed as inputs and taking into account the upper and lower limits of the wind power for modeling its uncertainties. In the robust model, the range of wind power is divided into several consecutive nested subintervals. The proposed ICHTF is simulated on a benchmark small-scale industrial sector which is located in a tropical region in order to minimize total electricity cost considering the operational constraints of Stirling cycle and AAHP considering the wind generation uncertainties. Simulation results reveal the feasibility and strength of the proposed ICHTF by solving a RNLP (robust nonlinear programming) problem using the CONOPT solver under GAMS (general algebraic mathematical system) environment.