Multiple Demand Response Research Articles

Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties

Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively.

Demand Response in Buildings: A Comprehensive Overview of Current Trends, Approaches, and Strategies

Power grids in the 21st century face unprecedented challenges, including the urgent need to combat pollution, mitigate climate change, manage dwindling fossil fuel reserves, integrate renewable energy sources, and meet greater energy demand due to higher living standards. These challenges create heightened uncertainty, driven by the intermittent nature of renewables and surges in energy consumption, necessitating adaptable demand response (DR) strategies. This study addresses this urgent situation based on a statistical analysis of recent scientific research papers. It evaluates the current trends and DR practices in buildings, recognizing their pivotal role in achieving energy supply–demand equilibrium. The study analysis provides insight into building types, sample sizes, DR modeling approaches, and management strategies. The paper reveals specific research gaps, particularly the need for more detailed investigations encompassing building types and leveraging larger datasets. It underscores the potential benefits of adopting a multifaceted approach by combining multiple DR management strategies to optimize demand-side management. The findings presented in this paper can provide information to and guide future studies, policymaking, and decision-making processes to assess the practical potential of demand response in buildings and ultimately contribute to more resilient and sustainable energy systems.

A Bi-Level Decision Framework for Incentive-Based Demand Response in Distribution Systems

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.

A four‐stage framework for optimal scheduling strategy of smart prosumers with vehicle‐to‐home capability under real time pricing based on interval optimization

AbstractWith the emergence of the Smart Grid concept, utility companies require more active participation of home users in the power sector. This changing paradigm is enabled by the wide deployment of multiple home assets such as small renewable‐based generators or storage facilities. In this context, consumers are no longer conceived as pure loads but also active agents that can exchange energy with the grid. To promote this active participation, utility companies promote different price‐based demand response programs to change the consumer patterns on pursuing a more efficient and economic system operation. In this regard, home energy management programs are becoming an essential tool for efficiently managing the different home users while addressing multiple demand response goals at minimum cost. In essence, a home energy management system is a computational optimization tool, which has to handle multiple uncertainties brought by weather forecast or energy pricing. This paper tackles this issue by developing a novel robust home energy management program based on interval optimization. In contrast to other related approaches, the proposal avoids the explicit use of interval arithmetic. Instead, the different uncertain parameters are sequentially incorporated into the scheduling task through different stages and interval‐based formulation. The developed methodology incorporates weather, load, energy pricing and plug‐in electric vehicle related uncertainties. A benchmark case study in a smart prosumer layout serves to prove the effectiveness of the new approach.

Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions

In the traditional power system, customers respond to their primary electricity consumption pattern based on price or incentive to take additional advantages. By developing energy hubs (EHs) where electricity, heat, natural gas and other forms of energy are coupled together, all types of energy customers, even the inelastic loads, can participate in the demand response (DR) program. This novel vision has led to the concept of “integrated demand response (IDR)”. IDR programs (IDRPs) in EHs involve coordinating multiple DR activities across different energy systems, such as buildings, industrial complexes and transportation networks. The main purpose of IDR is so that multi-energy users can respond not only by shifting or reducing their energy consumption from the demand side, but also by changing the type of energy consumed in response to the dispatching center. The integration of IDRPs in EHs can help to reduce energy costs, improve grid stability and increase the penetration of renewable energy sources (RES) in the power system. Moreover, by synchronizing DR activities across different energy systems, IDRPs can provide additional benefits, such as improved energy efficiency, reduced greenhouse gas emissions and increased resilience to power outages and other disruptions. In this paper, we provide an overview of the IDRP across EH areas, encompassing different aspects of it. First, the nature behind IDRP and its basic concept is introduced. Then, a categorization of fundamental principles within the IDRP is undertaken. Furthermore, modelling formulation and optimization techniques of IDRP in EHs are conducted. In addition to the IDRP content and model, this article deals with the research performed in this field from different perspectives. Finally, the advantages and prospect challenges of IDRPs are discussed.

Micro-Grid Day-Ahead Stochastic Optimal Dispatch Considering Multiple Demand Response and Electric Vehicles

Multiple demand responses and electric vehicles are considered, and a micro-grid day-ahead dispatch optimization model with photovoltaic is constructed based on stochastic optimization theory. Firstly, an interruptible load model based on incentive-based demand response is introduced, and a demand response mechanism for air conditioning load is constructed to implement an optimal energy consumption curve control strategy for air conditioning units. Secondly, considering the travel demand and charging/discharging rules of electric vehicles, the electric vehicle optimization model is built. Further, a stochastic optimization model of micro-grid with demand response and electric vehicles is developed because of the uncertainty of photovoltaic power output. Finally, the simulation example verifies the effectiveness of the proposed model. The simulation results show that the proposed model can effectively tackle the uncertainty of photovoltaic, as well as reduce the operating cost of micro-grid. Therefore, the effective interaction between users and electric vehicles can be realized.

Operational Issues of Contemporary Distribution Systems: A Review on Recent and Emerging Concerns

Distribution systems in traditional power systems (PS) constituted of passive elements and the distribution issues were then limited to voltage and thermal constraints, harmonics, overloading and unbalanced loading, reactive power compensation issues, faults and transients, loss minimization and frequency stability problems, to name a few. Contemporary distribution systems are becoming active distributed networks (ADNs) that integrate a substantially increasing amount of distributed energy resources (DERs). DERS include distributed generation (DG) sources, energy storage resources and demand side management (DSM) options. Despite their evidenced great benefits, the large-scale deployment and integration of DERs remain a challenge as they subsequently lead to the network operational and efficiency issues, hampering PS network reliability and stability. This paper carries out a comprehensive literature survey based on the last decade’s research on operational challenges reported and focusing on dispatchable and non-dispatchable DGs grid integration, on various demand response (DR) mechanisms and, on battery energy storage system (BESS) charging and discharging challenges, with the aim to pave the way to developing suitable optimization techniques that will solve the coordination of multiple renewable sources, storage systems and DRs to minimize distribution systems’ operational issues and thus improve stability and reliability. This paper’s findings assist the researchers in the field to conduct further research and to help PS planners and operators decide on appropriate relevant technologies that address challenges inherent to DG grid integration.

Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid

Integrating renewable energy resources (RES) is a challenge for power system operators due to their fluctuations and unpredictability. At the same time, the water shortage problem and the needs to desalinate more freshwater increase the prominence of sufficient energy resources for sustainable operation. Therefore, this paper presents a market-clearing mechanism in a co-optimization model that coordinates the operation of grid-connected reverse osmosis water desalination plants (RO-WDPs) and the operation of renewable-rich power systems. It is assumed that electric demands can participate in the provision of demand response (DR) to the market via multiple DR options. The DR options related to the general loads are demand shifting, load curtailment, distributed generation, and hybrid energy storage systems (HESS). These HESS includes battery storage and hydrogen storage systems. Pertaining to their special characteristics, a new DR option is proposed as a customized option for RO-WDPs. The market clearing mechanism is assumed to be based on the security-constrained unit commitment (SCUC) and formulated as a stochastic mixed-integer linear programming problem (MILP) to optimally schedule the day-ahead operation of the power system. The presented model is applied on 6-bus and IEEE 24-bus reliability test system (RTS) test power systems with significant penetration of RES to demonstrate its merits. The simulation results show that the system efficiency is enhanced by adding the energy flexibility of RO-WDP without endangering the water demand-supply when using the proposed coordinated model. Hence, the total operation cost is minimized, the RES integration is facilitated, and the hourly electricity prices are smoothened.

Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design

Coupon Incentive-based Demand Response (CIDR) is a novel type of demand response mechanism targeting at small residential end-consumers, and has advantages over traditional Demand Response (DR) programs in its adaptability to most electricity retail plans, voluntary and without penalty. CIDR’s three-layer mechanism was modeled with Stackelberg Game in the authors’ previous studies, and its Nash Equilibrium is usually obtained by iterative solution. However, there is limited study on the efficiency and social welfare of the traditional CIDR mechanism. This paper provides the following contributions: (1) Introducing the response function of consumers and Load Serving Entities (LSEs) considering bounded rationality, and modeling the demand response resources as Virtual Power Plants (VPPs) to find the analytical solution; (2) By comparing the response function with centralized dispatch, this paper reveals the social welfare loss under the Nash Equilibrium of current CIDR mechanism; (3) An Extra Incentive Term (EIT) awarded to the LSE was designed to maximize the social welfare, and its form and parameters are determined under different conditions such as multiple DRs and LSEs, as well as congested power system. The case study shows that EIT has successfully helped to improve the social welfare as well as increased the retail saving and suppressed Locational Marginal Prices in most scenarios.

Two-time-scale dispatch strategy for networked micro-grids considering uncertainties and demand response

Under the vision of Carbon Neutrality, multi-energy networked micro-grids (MGs) becomes a reliable and clean solution to achieve this goal. High penetration of renewable energy resources (RERs) brings great uncertainties to stable operation. Uncertainties in day-ahead scheduling must not be ignored. Also, demand response (DR) is getting increasing attentions in modern electricity market. In this regard, this study proposes a two-time-scale dispatch strategy for networked MGs considering multiple uncertainties and DR events. Both electric and thermal demands are taken into account. This strategy encompasses two stages, uncertainties considered day-ahead stage and DR considered intraday stage. The former stage combines scenarios analysis and budgeted robust methods to provide a day-ahead scheduling plan with robustness. The latter stage is an online model to deal with the newly updated forecast data and DR events. Adjustment cost is employed to measure the rescheduling cost. Emission governance cost has already been considered in the both stages, which contributes to realisation of carbon-neutral society. Four cases are simulated to verify performances of the two-time-scale dispatch strategy. Results show this strategy can effectively provide stable energy supply in networked mode. There are 12.0 MWh electricity and 11.8 MWh heat transmitted among MGs to guarantee reliability. Compared with nominal dispatch, 4.34% in average surplus electricity will be generated for robustness. And 19, 13 and 9 times constraint violation for each MG can be avoided in the robust day-ahead scheduling plan. Moreover, the purchased electricity from the grid also has a distinct reduction. It can be seen from the numerical simulation results that one MG can reduce 15.1% and 11.1% of the agreed electricity in the DR intervals. This proposed strategy does not only help MGs decrease the operation cost but also aid utility company relieving the peak demands.

Optimal Planning of Residential Microgrids Based on Multiple Demand Response Programs Using ABC Algorithm

The smart grid has revolutionized the conventional electricity grid with the proposition of demand-side management (DSM). A DSM program enables the user to schedule its energy consumption in compliance with any pricing signal. This scheduling helps the grid operator to reduce the peak load demand and jointly benefits the user to reduce its electricity costs. Despite that, while doing so, it jeopardizes the user’s comfort. In the present paper, the authors have investigated the impact of communal DSM programs on the consumption patterns of users, including single as well as multiple households. The objective is to simultaneously minimize the electricity costs and user discomfort to make a win-win situation for both the grid operator and the user. Therefore, a multi-objective optimization problem (MOOP) has been formed to simultaneously minimize the daily electricity cost, peak to average ratio (PAR) of load demand, user discomfort, environmental emission, and total net present cost (TNPC). In order to evaluate the best scheduling method, sizing scenarios for a residential microgrid in a Southern Pakistani metropolis surrounded by rural areas are presented in this paper. The originality of this article comes from a comparison of the techno-economic and environmental performance of several sizing options for a residential load powered by renewable energy. The artificial bee colony (ABC) algorithm has been selected to solve the MOOP. The DSM programs are based upon different pricing signals, including real-time electricity pricing (RTEP), critical peak pricing (CPP), time of use (TOU), and day-ahead pricing (DAP) pricing. The results of the proposed ABC algorithm are compared with GA and standard algorithms, and they reveal the effectiveness of the proposed method. When demand response is used, the suggested optimization technique shows that the SH spring with PV/WG/grid-connected microgrid is the most investable-reliable sizing option with a minimal TNPC of $1405.18 for DAP tariff with SH spring. Additionally, with a reduction in emissions of 6699 kg/yr, DAP tariff with SH spring shows that PV/WG/DG/grid-connected system has the greatest impact on the environment. For DAP tariff with SH spring, optimal sizes of PV, WG and converter are 26.7 kW, 30 kW and 6.67 kW, respectively.

Analysis of Microgrid’s Operation Integrated to Renewable Energy and Electric Vehicles in View of Multiple Demand Response Programs

A suitable energy management scheme and integrating renewable energy resources (RERs) can significantly increase energy efficiency and the stability of future grids operation. This work modeled a household energy management comprising a microgrid (MG) system and demand response programs (DRPs). Residential loads with price-based tariffs are introduced to reduce peak load demands and energy costs. For incorporating the uncertainties in RERs, their stochastic nature is modeled with a probabilistic method. This paper proposes a joint optimization approach for the optimal planning and operation of grid-connected residential, rural MG integrated into renewable energy and electric vehicles (EVs) in view of DRPs. The investigation focuses on energy saving of residential homes under different DRPs and RERs integration. The EVs are integrated into MG by including photovoltaic (PV), wind turbine (WT), fuel cell (FC), and diesel engines (DEs). A multi-objective optimization problem has been formulated to minimize the operating cost, pollutant treatment cost, and carbon emissions cost defined as C1, C2, and C3, respectively. The load demand has been rescheduled because of three DRPs, i.e., critical peak pricing (CPP), real-time electricity pricing (RTEP), and time of use (TOU). Further, the EV load has also been analyzed in autonomous and coordinated charging strategies. Using a judgement matrix, the proposed multi-objective problem is transformed into a single-objective problem. The results of an artificial bee colony (ABC) algorithm are compared with the particle swarm optimization (PSO) algorithm. The simulation analysis was accomplished by employing ABC and PSO in MATLAB. The mathematical model of MG was implemented, and the effects of DRPs based MG were investigated under different numbers of EVs and load data to reduce different costs. To analyze the impact of DRPs, the residential, rural MG is implemented for 50 homes with a peak load of 5 kW each and EV load with 80 EVs and 700 EVs, respectively. The simulation results with different test cases are formulated while analyzing the tradeoff between ABC and PSO algorithms. The simulation analysis shows that multiple DRPs, EVs, and RERs offered a substantial trade-off.

Demand Response as a Service: Clearing Multiple Distribution-Level Markets

The uncertain and non-dispatchable nature of renewable energy sources renders Demand Response (DR) a critical component of modern electricity distribution systems. Demand Response (DR) service provision takes place via aggregators and special distribution-level markets (e.g., flexibility markets), where small, distributed DR resources, such as building energy management systems, electric vehicle charging stations, micro-generation and storage, connected to the low-voltage distribution grid, offer DR services. In such systems, energy balancing (and thus, also DR decisions) have to be made close to real-time. Thus, market clearing algorithms for DR service provision must fulfill several requirements related to the efficiency of their operation. More specifically, a DR market clearing algorithm needs to be optimal in terms of cost-efficiency, scalable in terms of number of assets and locations, and able to satisfy real-time constraints. In order to cope with these challenges, this article presents a distributed DR market clearing algorithm based on Lagrangian decomposition, combined with an optimal cloud resource allocation algorithm for assigning the required computation power. A heuristic algorithm is also presented, able to achieve a near-optimal solution, within negligible computational time. Simulations, performed on a testbed, demonstrate the computational burden introduced by various DR models, as well as the heuristic algorithm's near-optimal performance. The resource allocation algorithm is able to service multiple DR requests (e.g., in multiple distribution networks), and minimize the cost of computational resources while respecting the execution time constraints of each request. This enables third parties to offer cost-efficient and competitive DR operation as a service.

Multi‐objective electrical demand response policy generation considering customer response behaviour learning: An enhanced inverse reinforcement learning approach

Demand response (DR) is an effective load management method. To attract customers to participate, DR policies need to both satisfy customers' individual DR habits and be economically profitable. However, customers’ individual DR habits are hard to be formulated with few hypotheses when other objectives are simultaneously considered. To tackle this challenge, a novel DR behavioural learning method is proposed. We learn customers’ DR habits by an inverse reinforcement learning (IRL) method to reduce the subjectivity in DR model formulation. Meanwhile, in contrast to traditional learning-based methods, the proposed method can adapt to multiple DR objectives more than just following customers’ DR habits, like obtaining higher economic revenues. Additionally, we consider the diversity and changes of customer DR behaviour patterns and offer an enhancement for the proposed DR behavioural learning method via building a DR pattern clustering and inference module. The proposed method can work with customer-side energy storage systems to diversify the DR policies and make the DR behaviours more flexible. Case studies show the proposed method can reduce about 10–20% behavioural learning deviations than the compared model-based methods, while daily charges of the proposed method can be further reduced by over 4% than the compared supervised-learning-based methods.

Data-oriented distributed demand response optimization with global inequality constraints based on multi-agent system

This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader’s strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play.

Research on Multiple Demand Response in Integrated Energy System Containing Power, Gas and Heat

Integrated demand response provides an important direction for realizing the bidirectional-interaction between supply and demand in the integrated energy system. In view of the differences in the time, space, cost and other aspects of users’ demand for multi-energy, a integrated demand response method based on the energy pricing strategy of the integrated energy system is proposed. Firstly, the characteristics of integrated demand response resources are analyzed, and various integrated demand response models are established. Secondly, based on the nodal energy price, considering the energy response potential, and taking the user energy cost as the optimization goal, an energy cost model that takes into account the load change of the user’s integrated demand response is established. Finally, an example is used to analyze the multi-energy load response under the integrated energy system nodal energy price strategy, which is beneficial to promote the rational use of energy by multi-energy users and reduce energy costs.

An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties

The combined cooling, heating and power (CCHP) microgrid has the advantages of promoting cleaner production and improving energy utilization efficiency. With the development of renewable energy sources (RES), it is more and more significant to study the optimal operation of CCHP based on the uncertainties of RES outputs. This paper proposes a CCHP-P2G microgrid system, which combined the power-to-gas (P2G) device with traditional CCHP microgrid. And a Data-driven Set based robust optimization (DSRO) model considering the uncertainties of wind power and multiple demand response programs (DRPs) have been presented, which consists of two stages: Day-ahead dispatching stage and Real-time adjusting stage. Simulations are delivered to show the following outcomes: (1) the P2G device can improve the electricity-gas coupling in the CCHP-P2G system, enhancing the system’s stability and economy of the system operation; (2) the multiple DRPs in the DSRO model can play the role of peak shaving and valley filling of electrical load, heating load and cooling load, so as to further reduce the operating cost of the system; (3) the DSRO model can resist the interferences of uncertain wind power outputs and keep both the conservativeness and computational complexity in relatively low levels.

Dynamic pricing in electricity and natural gas distribution networks: An EPEC model

Abstract Independent clearing of coupled retail electricity and natural gas markets generally results in a loss of market efficiency. This study investigates the problem of dynamic pricing in retail electricity and natural gas markets in the presence of network constraints by developing a two-leader multiple-follower bi-level model. Here, upper-level electricity and gas utility companies serve as leaders in the model, and determine their optimal dynamic retail prices in anticipation of the demand response (DR) provided by multiple lower-level integrated third-party DR providers or aggregators. Moreover, the model considers the dynamic gas prices provided by gas utility companies to electric utility companies for distributed gas-fired power units, which coordinates the operations of the separate utilities. The resulting model is recast as an equilibrium problem with equilibrium constraints (EPEC). Numerical results for a test system composed of an electric grid with distributed gas-fired power units, a gas distribution network, and multiple integrated DR aggregators indicate that the economic value of the proposed dynamic pricing scheme is up to 5.5% compared with existing regular pricing schemes.

A Wasserstein based two-stage distributionally robust optimization model for optimal operation of CCHP micro-grid under uncertainties

Combined cooling, heating and power (CCHP) micro-grids are getting increasing attentions due to the realization of cleaner production and high energy efficiency. However, with the features of complex tri-generation structure and renewable power uncertainties, it is challenging to effectively optimize the operation of CCHP micro-grid. This paper proposed a novel Wasserstein based two-stage distributionally robust optimization (WTSDRO) model for the day-ahead optimal operation of CCHP micro-grid. The uncertainties of wind power (or other renewable energy sources with random power output) forecasting errors are modeled as an ambiguity set based on Wasserstein metric, which is assumed to contain all the possible probability distributions with a confidence level. In the first stage, CCHP micro-gird’s operation cost is minimized according to the forecast information. In the second stage, for hedging against the perturbation of random wind power outputs, flexible resources are adjusted under the worst-case distribution within the ambiguity set. Multiple demand response programs (DRPs) are integrated to make electrical, thermal and cooling loads controllable. Finally, a reformulation approach is proposed based on strong duality theory, which equivalently transforms the WTSDRO model into a tractable MILP framework. Simulations implemented on a typical-structure CCHP micro-grid are delivered to show that our proposed model: (1) is data-driven and keeps both of the conservativeness and computational time at relatively low levels, (2) reaches effective operation results in terms of cost optimization, wind power accommodation and waste heat utilization etc. Moreover, operation cost and CO2 emission can be further saved by integrating multiple DRPs.

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction.